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Social Network Monitoring for Bursty Cascade Detection

Exploring Multiobjective Optimization for Multiview Clustering

Generating Realistic Synthetic Population Datasets

Motif Counting Beyond Five Nodes

Exploiting User Posts for Web Document Summarization

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About TKDD 

ACM Transactions on Knowledge Discovery from Data (TKDD) publishes original archival papers in the area of knowledge discovery from data and closely related disciplines.  The majority of the papers that appear in TKDD is expected to address the logical and technical foundation of knowledge discovery and data mining.

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Forthcoming Articles
Spatio-Temporal Routine Mining on Mobile Phone Data

Mining human behaviors has always been an important subarea of Data Mining. While it often serves as empirical evidence in psychological/behavioral studies, it also builds the foundation of various big-data systems which rely heavily on the prediction of human behaviors. In recent years, the ubiquitous spreading of mobile phones and the massive amount of spatio-temporal data collected from them make it possible to keep track of the daily traveling behaviors of mobile subscribers and further conduct routine mining on them. In this paper, we propose to model mobile subscribers' daily traveling behaviors by three levels: location trajectory, one-day pattern and routine pattern. We develop the model Spatio-Temporal Routine Mining Model (STRMM) to characterize the generative process between these three levels. From daily trajectories, the STRMM model unsupervisedly extracts spatio-temporal routine patterns that contain double information: 1) how people's typical traveling patterns are. 2) how much their traveling behaviors vary from day to day. Compared to traditional methods, STRMM takes into account the different degrees of behavioral uncertainty in different timespans of a day, yielding more realistic and intuitive results. To learn model parameters, we adopt Stochastic Expectation Maximization algorithm. Experiments are conducted on two real world datasets, and the empirical results show that the STRMM model can effectively discover hidden routine patterns of human traveling behaviors and yields higher accuracy results in trajectory prediction task.

Cluster's quality evaluation and selective clustering ensemble

Clustering ensemble has drawn much attention in recent years due to its ability to generate a high quality and robust partition result. Weighted clustering ensemble and selective clustering ensemble are two general ways to further improve the performance of a clustering ensemble method. Existing weighted clustering ensemble methods assign the same weight to each cluster in a partition of the ensemble. Since the qualities of the clusters in a partition are different, the clusters should be weighted differently. To address this issue, this paper proposes a new measure to calculate the similarity between a cluster and a partition. Theoretically, this measure is effective in handling two problems in measuring the quality of a cluster, which are defined as the symmetric problem and the context meaning problem. In addition, some properties of the proposed measure are analyzed. This measure can be easily expanded to a clustering performance measure that calculates the similarity between two partitions. As a result of this measure, we propose a novel selective clustering ensemble framework, which considers the differences between the objective of the ensemble selection stage and the object of the ensemble integration stage in the selective clustering ensemble. To verify the performance of the new measure, we compare the performance of the measure with two existing measures in weighting clusters. The experiments show that the proposed measure is more effective. To verify the performance of the novel framework, four existing selective clustering ensemble frameworks are employed as references. The experiments show that the proposed framework is statistically better than the others on ten UCI benchmark data sets, six document data sets, and the Olivetti Face Database.

Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations

A problem of reducing interval uncertainty is considered by an approach of cutting off equal parts from the left and right. The interval contains admissible values of an observed objects parameter. The objects parameter cannot be measured directly or deductively computed, so it is estimated by expert judgments. The task is to map a set of admissible values of the objects parameter (the initial interval) into a set of practicable values of this parameter. Redundant (irrelevant) values are removed according to experts judgments. Terms of observations are short, and the objects statistical data are poor. Any statistical methods for reducing the interval uncertainty are unreliable due to the term of the parameters application tends to be the shortest. Thus an algorithm of flexibly reducing interval uncertainty is designed via adjusting the parameter by expert procedures and allowing to control cutting off. The interval reduction ensues from the adjustment. While the parameter is adjusted forward, the interval becomes progressively narrowed after every next expert procedure. The narrowing is performed via division-by-q dichotomization cutting off the (1/q)-th parts from the left and right. If the current parameters value falls outside of the interval, forward adjustment is canceled. Then backward adjustment is executed, where one of the endpoints is moved backwards. Rough (hard) and smooth (soft) backward movings are provided. If the current parameters value belonging to the interval is too close to either left or right endpoint, then this endpoint is not moved. The closeness is treated differently from both sides by the given relative tolerances. Adjustment is not executed when the current parameters value enclosed within the interval is simultaneously too close to both left and right endpoints. If the current parameters value is trapped like that for a definite number of times in succession, the early stop fires. That definite number serves to reach the statistical stability.

Large-scale Adversarial Sports Play Retrieval with Learning to Rank

As teams of professional leagues are becoming more and more analytically driven, the interest in e ective data management and access of sports plays has dramatically increased. In this paper, we present a retrieval system that can quickly nd the most relevant plays from historical games given an input query. To search through a large number of games at an interactive speed, our system is built upon a distributed framework so that each query-result pair is evaluated in parallel. We also propose a pairwise learning to rank approach to improve search ranking based on users' clickthrough behavior. The similarity metric in training the rank function is based on automatically learnt features from a convolutional autoencoder. Finally, we showcase the e cacy of our learning to rank approach by demonstrating rank quality in a user study.

Representation Learning for Classification in Heterogeneous Graphs with Application to Social Networks

We address the task of node classification in heterogeneous networks, where the nodes may be of different types, each type with its own set of labels, and relations between nodes may also be of different types. A typical example is provided by social networks where the node types are e.g. users, content, films, etc. and relations may be \emph{friendship}, \emph{like}, \emph{authorship}, etc. Learning and performing inference on such heterogeneous networks is a recent task requiring new models and algorithms. We propose a model, \textbf{Labeling Heterogeneous Network (LaHNet)}, a transductive approach to classification that learns to project the different types of nodes into a common latent space. This embedding is learned so as to reflect different characteristics of the problem such as the correlation between labels of nodes with different types and the graph topology. The application focus is on social graphs but the algorithm is general and could be used for other domains as well. The model is evaluated on five datasets representative of different instances of social data.

Entity Based Query Recommendation for Long-Tail Queries

Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users search history (or query logs), are often insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study the use of entities found in queries to provide recommendations. Specifically, we extract entities from a query, and use these entities to explore new ones by consulting an information source. The discovered entities are then used to suggest new queries to the user. In this paper, we examine two information sources: (1) a knowledge base (or KB), such as YAGO and Freebase; and (2) a click log, which contains the URLs accessed by a query user. We study how to use these sources to find new entities useful for query recommendation. We further study a hybrid framework that integrates different query recommendation methods effectively. As shown in the experiments, our proposed approaches provide better recommendations than existing solutions for long-tail queries. In addition, our query recommendation process takes less than 100ms to complete. Thus, our solution is suitable for providing online query recommendation services for search engines.

Coordination Event Detection and Initiator Identification in Time Series Data

Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species. In this work, we formalize the Coordination Event Detection and Initiator Identification in Time Series Data and propose a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods. The proposed approach, given arbitrary individual time series, automatically (1) identifies times of coordinated group activity, (2) determines the identities of initiators of those activities, and (3) classifies the likely mechanism by which the group coordination occurred, all of which are novel computational tasks. We demonstrate our framework on both simulated and real-world data: trajectories tracking of animals as well as stock market data. Our method is competitive with existing global leadership inference methods but provides the first approaches for local leadership and coordination mechanism classification. Our results are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our method is easily generalizable to any coordinated time-series data from interacting entities.

Event Analytics via Discriminant Tensor Factorization

Analyzing the impact of disastrous events has been central in understanding and responding to crises. Traditionally, the assessment of disaster impact has primarily relied on the manual collection and analysis of surveys and questionnaires as well as the review of authority reports. This can be costly and time-consuming whereas a timely assessment of an event's impact could be critical for crisis management and humanitarian operations. In this work, we formulate the impact discovery as the problem to identify the shared and discriminative subspace via tensor factorization due to the multidimensional nature of mobility data. Existing work in mining the shared and discriminative subspaces typically requires the predefined number of either type of them. In the context of event impact discovery, this could be impractical, especially for those unprecedented events. To overcome this, we propose a new framework, called ``PairFac,'' that jointly factorizes the multidimensional data to discover the latent mobility pattern along with its associated discriminative weight. This framework enables the removal of efforts in splitting the shared and discriminative subspaces in advance and at the same time automatically captures the persistent and changing patterns from multidimensional behavioral data. Our work has important applications in crisis management, which provides a timely assessment of impacts of major events in the urban environment.

Sequential Feature Explanations for Anomaly Detection

In many applications, an anomaly detection system presents the most anomalous data instance to a human analyst, who then must determine whether the instance is truly of interest (e.g. a threat in a security setting). Unfortunately, most anomaly detectors provide no explanation about why an instance was considered anomalous, leaving the analyst with no guidance about where to begin the investigation. To address this issue, we study the problems of computing and evaluating sequential feature explanations (SFEs) for anomaly detectors. An SFE of an anomaly is a sequence of features, which are presented to the analyst one at a time (in order) until the information contained in the highlighted features is enough for the analyst to make a confident judgement about the anomaly. Since analyst effort is related to the amount of information that they consider in an investigation, an explanation's quality is related to the number of features that must be revealed to attain confidence. In this paper, we first formulate the problem of optimizing SFEs for a particular density-based anomaly detector. We then present both greedy algorithms and an optimal algorithm, based on branch-and-bound search, for optimizing SFEs. Finally, we provide a large scale quantitative evaluation of these algorithms using a novel framework for evaluating explanations. The results show that our algorithms are quite effective and that our best greedy algorithm is competitive with optimal solutions.

SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRank

Automatic Term Extraction deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that no existing methods can consistently outperforms others in all domains. This work adopts a different strategy towards this problem as we propose to `enhance' existing ATE methods instead of `replace' them. We introduce SemRe-Rank, a generic method based on the concept of incorporating semantic relatedness - an often overlooked venue - into an existing ATE method to further improve its performance. SemRe-Rank applies a personalized PageRank process to a semantic relatedness graph of words to compute their `semantic importance' scores, which are then used to revise the scores of term candidates computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all methods and across all datasets. The best performing variants of SemRe-Rank have achieved, on some datasets, an improvement of 0.15 (on a scale of 0 $\sim$ 1.0) in terms of the precision in the top ranked $K$ term candidates, and an improvement of 0.28 in terms of overall F1.

Stability and Robustness in Influence Maximization

We study the robustness of Influence Maximization models and algorithms to noise in the input data. Such robustness is essential if Influence Maximization is to live up to its claimed potential of providing real-world social or financial benefits. First, we exhibit simple inputs on which even very small estimation errors may mislead every algorithm into highly suboptimal solutions. Motivated by this observation, we propose the Perturbation Interval Model as a framework to characterize the stability of Influence Maximization against noise in the inferred diffusion network. Analyzing the susceptibility of specific instances to estimation errors leads to a clean algorithmic question, which we term the Influence Difference Maximization problem. However, the objective function of Influence Difference Maximization is NP-hard to approximate within a factor of O(n1-µ) for any µ > 0. Given the infeasibility of diagnosing instability algorithmically, we focus on finding influential users robustly across multiple diffusion settings. We define a Robust Influence Maximization framework wherein an algorithm is presented with a set of influence functions. The algorithm's goal is to identify a set of k nodes who are simultaneously influential for all influence functions, compared to the (function-specific) optimum solutions. We show strong approximation hardness results for this problem unless the algorithm gets to select at least a logarithmic factor more seeds than the optimum solution. However, when enough extra seeds may be selected, we show that techniques of Krause et al. can be used to approximate the optimum robust influence to within a factor of 1-1/e. We evaluate this bicriteria approximation algorithm against natural heuristics on several real-world data sets. Our experiments indicate that the worst-case hardness does not necessarily translate into bad performance on real-world data sets; all algorithms perform fairly well.

Employing Semantic Context for Sparse Information Extraction Assessment

A huge amount of texts available on the World Wide Web presents an unprecedented opportunity for Information Extraction. One important assumption in information extraction is that frequent extractions are more likely to be correct. Sparse Information Extraction is hence a challenging task because no matter how big a corpus is, there are extractions supported by only a small amount of evidence in the corpus. However, there is limited research on sparse information extraction especially in the assessment of the validity of sparse information extractions. Motivated by this, we introduce a lightweight, explicit semantic approach for assessing sparse information extraction1. We rstly use a large semantic network consisting of millions of concepts, entities, and attributes to explicitly model the context of any semantic relationship. Secondly, we learn from three semantic contexts using dierent base classiers to select an optimal classication model for assessing sparse extractions. Finally, experiments show that as compared with several state-of-the-art approaches, our approach can signicantly improve the F-score in the assessment of sparse extractions while maintaining the efficiency.

ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages

We present ABRA, a suite of algorithms to compute and maintain probabilistically-guaranteed high-quality approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs. Our algorithms use progressive random sampling and their analysis rely on Rademacher averages and pseudodimension, fundamental concepts from statistical learning theory. To our knowledge, this is the rst application of these concepts to the eld of graph analysis. Our experimental results show that ABRA is much faster than exact methods, and vastly outperforms, in both runtime number of samples, and accuracy, state-of-the-art algorithms with the same quality guarantees.

Protecting privacy in trajectories with a user-centric approach

The increased use of location-aware devices, such as smartphones, generates a large amount of trajectory data. These data can be useful in several domains, like marketing, path modeling, localization of an epidemic focus, etc. Nevertheless, since trajectory information contains personal mobility data, improper use or publication of trajectory data can threaten users' privacy. It may reveal sensitive details like habits of behavior, religious beliefs, and sexual preferences. Therefore, many users might be unwilling to share their trajectory data without a previous anonymization process. Currently, several proposals to address this problem can be found in the literature. These solutions focus on anonymizing data before its publication, i.e., when they are already stored in the server database. Nevertheless, we argue that this approach gives the user no control about the information she shares. For this reason, we propose anonymizing data in the users' mobile devices, before they are sent to a third party. This paper extends our previous work which was, to the best of our knowledge, the first one to anonymize data at the client side, allowing users to select the amount and accuracy of shared data. In this paper, we describe an improved version of the protocol, and we include the implementation together with an analysis of the results obtained after the simulation with real trajectory data.

Modeling Alzheimer's Disease Progression with Fused Laplacian Sparse Group Lasso

Alzheimer's disease (AD), the most common type of dementia, has been not only the substantial financial burden to the health care system but also the psychological and emotional burden to patients and their families. There is thus an urgent need to infer a trajectory of cognitive performance over time and identify biomarkers predictive of the progression. In this paper, we propose the multi-task learning with fused Laplacian sparse group lasso algorithm, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers (ADMM) based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the proposed algorithm is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.

Online Active Learning with Expert Advice

In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real world applications, acquiring the true labels of all instances can be both costly and time consuming, especially for large-scale problems. For example, in the social media, data stream usually comes in a high speed and volume, and it's nearly impossible and highly costly to label all of the instances. In this paper, we address this problem with active learning with expert advice, where the label information of an instance is disclosed only when it is requested by the proposed active online learners. Our goal is to minimize the queried instances while training an online learning model without sacrificing the performance. To address this challenge, we propose a framework of active forecasters, which attempts to extend two fully supervised forecasters, Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of online active learning with expert advice. Specifically, we proposed two online active learning with expert advice algorithms, named Active Exponentially Weighted Average Forecaster~(AEWAF) and Active Greedy Forecaster~(AGF), by considering the difference of expert advices. To further improve the robustness of the proposed AEWAF and AGF algorithms in the noisy scenarios~(where noisy experts exist), we also proposed two robust active learning with expert advice algorithms, named Robust Active Exponentially Weighted Average Forecaster~(RAEWAF) and Robust Active Greedy Forecaster~(RAGF). We validate the efficacy of the proposed algorithms by an extensive set of experiments in both normal scenario~(where all of experts are comparably reliable) and noisy scenario.

Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-based Ensembles

A recent experimental evaluation assessed 19 time series classification (TSC) algorithms and found that one was significantly more accurate than all others: the Flat Collective of Transformation-based Ensembles (Flat-COTE). Flat-COTE is an ensemble that combines 35 classifiers over four data representations. However, while comprehensive, the evaluation did not consider deep learning approaches. Convolutional neural networks (CNN) have seen a surge in popularity and are now state of the art in many fields and raises the question of whether CNNs could be equally transformative for TSC. We implement a benchmark CNN for TSC using a common structure and use results from a TSC-specific CNN in the literature. We compare both to Flat-COTE and find that the collective is significantly more accurate than both CNNs. These results are impressive, but Flat-COTE is not without deficiencies. We significantly improve the collective by proposing a new hierarchical structure with probabilistic voting, defining and including two novel ensemble classifiers built in existing feature spaces, and adding further modules to represent two additional transformation domains. The resulting classifier, the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE), encapsulates classifiers built on five data representations. We demonstrate that HIVE-COTE is significantly more accurate than Flat-COTE (and all other TSC algorithms that we are aware of) over 100 resamples of 85 TSC problems and is the new state of the art for TSC. Further analysis is included through the introduction and evaluation of 3 new case studies and extensive experimentation on 1000 simulated datasets of 5 different types.

Discovering mobile application usage patterns from a large-scale dataset

The discovering of patterns regarding how, when, and where users interact with mobile applications reveals important insights for mobile service providers. In this work, we exploit for the first time a real and large-scale dataset representing the records of mobile application usage of 5,342 users during 2014. The data was collected by a software agent, installed at the users' smartphones, which monitors detailed usage of applications. First, we look for general patterns of how users access some of the most popular mobile applications in terms of frequency, duration, diversity, and data traffic. Next, we mine the dataset looking for temporal patterns in terms of when and how often accesses occur. Finally, we exploit the location of each access to detect users' points of interest and location-based communities. Based on the results, we derive a model to generate synthetic datasets of mobile application usage. We also discuss a series of implications of the findings regarding telecommunication services, mobile advertisements, and smart cities. This is the first time this dataset is used, and we also make it publicly available for other researchers.

ClassiNet -- Predicting Missing Features for Short-Text Classification

Short and sparse texts such as tweets, search engine snippets, product reviews, chat messages are abundant on the Web. Classifying such short-texts into a pre-defined set of categories is a common problem that arises in various contexts, such as sentiment classification, spam detection, and information recommendation. The fundamental problem in short-text classification is \emph{feature sparseness} -- the lack of feature overlap between a trained model and a test instance to be classified. We propose \emph{ClassiNet} -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem. Using a set of unlabeled training instances, we first learn binary classifiers as feature predictors for predicting whether a particular feature occurs in a given instance. Next, each feature predictor is represented as a vertex $v_i$ in the ClassiNet where a one-to-one correspondence exists between feature predictors and vertices. The weight of the directed edge $e_{ij}$ connecting a vertex $v_i$ to a vertex $v_j$ represents the conditional probability that given $v_i$ exists in an instance, $v_j$ also exists in the same instance. We show that ClassiNets generalize word co-occurrence graphs by considering implicit co-occurrences between features. We extract numerous features from the trained ClassiNet to overcome feature sparseness. In particular, for a given instance $\vec{x}$, we find similar features from ClassiNet that did not appear in $\vec{x}$, and append those features in the representation of $\vec{x}$. Moreover, we propose a method based on graph propagation to find features that are indirectly related to a given short-text. We evaluate ClassiNets on several benchmark datasets for short-text classification. Our experimental results show that by using ClassiNet, we can statistically significantly improve the accuracy in short-text classification tasks, without having to use any external resources such as thesauri for finding related features.

Coupled Clustering Ensemble by Exploring Data Independence

In most of the existing clustering ensembles, there is a strong assumption on IIDness (i.e. independent and identical distribution), which states that base clusterings perform independently of one another and all objects are also independent. In the real-world, however, objects are likely related to each other through features that are either explicit or even implicit. There is also implicit relationship among intermediate base clusterings because they are derived from the same set of data. All these demand further investigation of clustering ensembles that explore this non-IIDness nature of data. In this paper, we extend existing clustering ensembles and propose the coupled clustering ensemble (CCE) that takes the non-IIDness nature of objects and intermediate base clusterings into consideration. The main idea is to explore: (i) the intra-dependence relationship between objects by aggregating the similarity of base clusterings, and (ii) the inter-dependence relationship among objects by exploring their neighborhood domains. Once these non-IIDness relationships are discovered, they will act as critical supplements to the clustering ensembles. We verified our proposal using three types of consensus function: clustering-based, object-based and cluster-based. Substantial experiments on two synthetic and nine UCI benchmark data sets demonstrate that the CCE can effectively capture the implicit dependence relationship among base clusterings and objects with higher clustering accuracy, stability, and robustness compared to ten state-of-the-art techniques. Finally, we also show that the final clustering quality is dependent on data characteristics (quality and consistency) of base clusterings.

Bibliometrics

Publication Years 2007-2018
Publication Count 350
Citation Count 3021
Available for Download 350
Downloads (6 weeks) 3289
Downloads (12 Months) 29611
Downloads (cumulative) 225083
Average downloads per article 643
Average citations per article 9
First Name Last Name Award
Foto Afrati ACM Fellows (2014)
Charu Chandra Aggarwal ACM Fellows (2013)
John Canny ACM Doctoral Dissertation Award (1987)
Carlos A. Castillo ACM Senior Member (2014)
Ming-Syan Chen ACM Fellows (2006)
Chris Clifton ACM Distinguished Member (2017)
ACM Senior Member (2006)
Graham R. Cormode ACM Distinguished Member (2013)
Christos Faloutsos ACM Fellows (2010)
Benjamin Fung ACM Senior Member (2013)
Johannes Gehrke ACM Fellows (2014)
Lee Giles ACM Fellows (2006)
John Guttag ACM Fellows (2006)
Jiawei Han ACM Fellows (2003)
John E Hopcroft ACM Karl V. Karlstrom Outstanding Educator Award (2008)
ACM Fellows (1994)
ACM A. M. Turing Award (1986)
Piotr Indyk ACM Fellows (2015)
ACM Paris Kanellakis Theory and Practice Award (2012)
Masaru Kitsuregawa ACM Fellows (2012)
Jon Kleinberg ACM AAAI Allen Newell Award (2014)
ACM Fellows (2013)
ACM Prize in Computing (2008)
Sarit Kraus ACM Fellows (2014)
Hans-Peter Kriegel ACM Fellows (2009)
Laks Lakshmanan ACM Distinguished Member (2016)
Ming Li ACM Fellows (2006)
Chih-Jen Lin ACM Fellows (2015)
ACM Distinguished Member (2011)
ACM Senior Member (2010)
Chang-Tien Lu ACM Distinguished Member (2015)
Tao Mei ACM Distinguished Member (2016)
ACM Senior Member (2012)
Filippo Menczer ACM Distinguished Member (2013)
S. Muthukrishnan ACM Fellows (2010)
Shamkant Navathe ACM Fellows (2014)
Sethuraman Panchanathan ACM Senior Member (2009)
Jian Pei ACM Fellows (2015)
ACM Senior Member (2007)
Ali Pinar ACM Distinguished Member (2015)
ACM Senior Member (2011)
Raghu Ramakrishnan ACM Fellows (2001)
Dan Roth ACM Fellows (2011)
Michael Rung-Tsong Lyu ACM Fellows (2015)
Domenico Sacca ACM Senior Member (2007)
J. Sander ACM Distinguished Member (2015)
Padhraic Smyth ACM Fellows (2013)
Divesh Srivastava ACM Fellows (2011)
John Stasko ACM Distinguished Member (2011)
ACM Senior Member (2011)
Jie Tang ACM Senior Member (2017)
Donald F Towsley ACM Fellows (1997)
Paolo Trunfio ACM Senior Member (2017)
Jeffrey D Ullman ACM Karl V. Karlstrom Outstanding Educator Award (1997)
ACM Fellows (1995)
Eli Upfal ACM Fellows (2005)
Limsoon Wong ACM Fellows (2013)
Hui Xiong ACM Distinguished Member (2014)
ACM Senior Member (2010)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)
Philip S Yu ACM Fellows (1997)
Mohammed Zaki ACM Distinguished Member (2010)
Ben Y. Zhao ACM Distinguished Member (2015)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)
Zhi-Hua Zhou ACM Fellows (2016)
ACM Distinguished Member (2013)
ACM Senior Member (2011)
Zhi-Hua Zhou ACM Fellows (2016)
ACM Distinguished Member (2013)
ACM Senior Member (2011)

First Name Last Name Paper Counts
Christos Faloutsos 15
Hui Xiong 7
Jieping Ye 7
John Lui 6
Jian Pei 5
Aristides Gionis 5
Tao Li 5
Hanghang Tong 4
Zhiwen Yu 4
Zhihua Zhou 4
Christopher Jermaine 4
Shenghuo Zhu 4
Heng Huang 4
John Hopcroft 4
Huan Liu 4
Bin Guo 4
Feiping Nie 4
Jilles Vreeken 4
Philip Yu 4
Nikolaj Tatti 4
Hong Cheng 4
Naren Ramakrishnan 3
Lise Getoor 3
Chengqi Zhang 3
Jure Leskovec 3
Malik Magdon-Ismail 3
Xiaoli Fern 3
Enhong Chen 3
Qi Liu 3
Mingsyan Chen 3
Lei Tang 3
Hamid Rabiee 3
Yasushi Sakurai 3
Yihong Gong 3
Yun Chi 3
Evimaria Terzi 3
Jirong Wen 3
Srinivasan Parthasarathy 3
Jennifer Neville 3
Fabio Fassetti 3
Dingding Wang 3
Fabrizio Angiulli 3
Guofei Jiang 3
Jimeng Sun 2
Laks Lakshmanan 2
Wei Ding 2
U Kang 2
Vivekanand Gopalkrishnan 2
Yong Ge 2
Belle Tseng 2
Jie Tang 2
Xianchao Zhang 2
Dacheng Tao 2
Kui Yu 2
Christopher Leckie 2
Hong Xie 2
Sanjay Ranka 2
Carlotta Domeniconi 2
Jiliang Tang 2
Martin Ester 2
Dantong Yu 2
Charalampos Tsourakakis 2
Eugene Agichtein 2
Bryan Hooi 2
Jon Kleinberg 2
Wei Wang 2
Wei Fan 2
Charu Aggarwal 2
Neil Shah 2
Joydeep Ghosh 2
Dino Pedreschi 2
Hari Sundaram 2
Eli Upfal 2
Mohamed Bouguessa 2
Spiros Papadimitriou 2
Kristina Lerman 2
Yehuda Koren 2
Heikki Mannila 2
Panayiotis Tsaparas 2
Kijung Shin 2
Stefan Kramer 2
Leman Akoglu 2
Chen Chen 2
Arnold Boedihardjo 2
Pinghui Wang 2
Hao Huang 2
Hong Qin 2
Yangqiu Song 2
Indrajit Bhattacharya 2
Panagis Magdalinos 2
Ali Khodadadi 2
Jingrui He 2
Geoffrey Webb 2
Wei Cheng 2
Peng Cui 2
Qiang Yang 2
Michalis Vazirgiannis 2
Junzhou Zhao 2
Xiaohong Guan 2
Arthur Zimek 2
Jianhui Chen 2
Xiao Yu 2
Zhu Wang 2
Jin Huang 2
Yu Zhang 2
Jiawei Han 2
JiLei Tian 2
Ping Luo 2
Brian Gallagher 2
B Prakash 2
Jie Tang 2
Sanjay Chawla 2
Ruoming Jin 2
Yanjie Fu 2
Petros Drineas 2
Shinjae Yoo 2
Yuru Lin 2
Ian Davidson 2
Andrea Esuli 2
Charles Ling 2
Antonella Guzzo 2
Steven Hoi 2
Jiawei Han 2
Daniel Kifer 2
Xiang Zhang 2
Alex Beutel 2
Pauli Miettinen 2
Waynexin Zhao 2
Lei Chen 2
Antônio Loureiro 2
Yan Liu 2
Hao Wu 2
Maryam Ramezani 2
Sucheta Soundarajan 2
Don Towsley 2
Rita Chattopadhyay 2
Matteo Riondato 2
Fabrizio Sebastiani 2
Prithwish Chakraborty 1
Wei Xie 1
Jing Xiao 1
Sayantan Mitra 1
MingXi Wu 1
Benjamin Fung 1
Ye Chen 1
John Canny 1
Jure Leskovec 1
Dominique Laurent 1
Yeowwei Choong 1
Shebuti Rayana 1
Michalis Faloutsos 1
Hongxia Yang 1
Haoda Fu 1
Dawei Zhou 1
Jingrui He 1
Liming Chen 1
Minsoo Jung 1
Frédéric Rayar 1
Sabine Barrat 1
Jessica Lin 1
Xing Wang 1
Ramakrishnan Kannan 1
Peng Mi 1
Arnab Nandi 1
Axel Soto 1
Boyue Wang 1
Ben London 1
Joseph Ruiz Md 1
Jevin West 1
Biao Xiang 1
Yi Zheng 1
Fei Yi 1
Ting Guo 1
Jia Wu 1
Xingquan Zhu 1
Xun Tang 1
Luca Becchetti 1
Ying Cui 1
Meghana Deodhar 1
Keli Xiao 1
Yiping Ke 1
Jenwei Huang 1
Kuan Zhang 1
Vetle Torvik 1
Jianping Zhang 1
Manas Somaiya 1
Shantanu Sharma 1
Lingjyh Chen 1
Linhong Zhu 1
Makoto Yamada 1
Guoqing Chen 1
Liang Hong 1
Hunghsuan Chen 1
Venu Satuluri 1
Rose Yu 1
Yao Zhang 1
Tianbing Xu 1
Sanmay Das 1
Phengann Heng 1
Alyson Wilson 1
Hua Wang 1
Gang Tian 1
Paolo Trunfio 1
Hunghsuan Chen 1
Flavio Chierichetti 1
Xiaowei Chen 1
Feida Zhu 1
Nashreen Nesa 1
Patrick Hung 1
Manolis Kellis 1
Feiyu Xiong 1
Fei Wang 1
Shiqiang Tao 1
Guoqiang Zhang 1
Julian McAuley 1
Pavel Senin 1
Sunil Gandhi 1
John Stasko 1
Hannah Kim 1
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Esther Galbrun 1
Junbin Gao 1
Yanfeng Sun 1
Baocai Yin 1
Jaideep Srivastava 1
João Gama 1
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Shiyou Qian 1
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Evrim Acar 1
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Jilei Tian 1
Davoud Moulavi 1
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Xueying Zhang 1
Junming Shao 1
Yllka Velaj 1
Xiaojun Chang 1
Lars Schmidt-Thieme 1
Luigi Moccia 1
Edoardo Serra 1
Gunjan Gupta 1
Ling Feng 1
Diana Inkpen 1
Tetsuji Ogawa 1
Yi Wang 1
Maha Alabduljalil 1
Carlos Castillo 1
Michele Coscia 1
Ming Zhang 1
Biru Dai 1
Li Wan 1
Weekeong Ng 1
Divesh Srivastava 1
Lian Duan 1
Bruno Ribeiro 1
Siyuan Liu 1
Amit Dhurandhar 1
Beechung Chen 1
Charles Elkan 1
Carlos Guestrin 1
Tomoharu Iwata 1
Naonori Ueda 1
Shipeng Yu 1
Maria Sapino 1
Qi Lou 1
Wei Fan 1
Xifeng Yan 1
Zhiting Hu 1
Pedro Melo 1
Yuan Jiang 1
Dengyong Zhou 1
Yang Zhou 1
Xinjiang Lu 1
Aisling Kelliher 1
Paul Castro 1
Anon Plangprasopchok 1
Shengrui Wang 1
Sethuraman Panchanathan 1
Michael Mampaey 1
Qinbao Song 1
Qinli Yang 1
Josif Grabocka 1
Nicolas Schilling 1
Xiang Li 1
David Aha 1
Richard Xu 1
Sougata Mukherjea 1
Ashwin Ram 1
Hungleng Chen 1
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Gianni Costa 1
Yuto Yamaguchi 1
Shaoxu Song 1
Guangneng HU 1
Michael Lyu 1
Dityan Yeung 1
Maya Bercovitch 1
Kaiming Ting 1
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Teresa Tjahja 1
Wenchih Peng 1
Zekai Gao 1
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Laura Smith 1
Jin Zhang 1
Chengxiang Zhai 1
Dong Xin 1
Christian Böhm 1
Adelelu Jia 1
Alexandru Iosup 1
Aniket Chakrabarti 1
Reza Zafarani 1
Saurabh Kataria 1
Shaojun Wang 1
Seyyed Hosseini 1
Erfan Tavakoli 1
Jordan Bakerman 1
Geoffrey Fairchild 1
Qi Tan 1
Stefano Leucci 1
Cheukkwong Lee 1
Mohammed Zaki 1
Tina Eliassi-Rad 1
Yanjun Qi 1
Theodoros Lappas 1
Munmun De Choudhury 1
Wenjie Li 1
Yada Zhu 1
Longjie Li 1
Bruno Abrahão 1
Xiaolin Wang 1
Tingting Gao 1
Yongsub Lim 1
Zhicheng Liu 1
Changhyun Lee 1
Niranjan Kamat 1
Haojun Zhang 1
Ganesh Ramesh 1
A Patterson 1
Yu Lei 1
Limsoon Wong 1
Fedja Hadzic 1
Elizabeth Chang 1
Aminul Islam 1
Jingchao Ni 1
Alceu Costa 1
Yihan Wang 1
Rinkl Rani 1
Matthew Rowe 1
Raheleh Makki 1
Maria De Oliveira 1
Zhongfei Zhang 1
Edward Chang 1
Shanshan Feng 1
Guannan Liu 1
Liang Wang 1
Kimon Fountoulakis 1
Jennifer Dy 1
Ayan Acharya 1
Sreangsu Acharyya 1
Changtien Lu 1
Zhiqiang Xu 1
Nitin Agarwal 1
S Muthukrishnan 1
Geoffrey Barbier 1
Loïc Cerf 1
Aditya Menon 1
Dafna Shahaf 1
Stephen Fienberg 1
Amin Saberi 1
Min Ding 1
Gensheng Zhang 1
Kazumi Saito 1
Raviv Raich 1
Bilson Campana 1
Vibhor Rastogi 1
Deng Cai 1
Yiming Yang 1
Vassilios Vassiliadis 1
Fei Zou 1
Virgílio Almeida 1
Christos Faloutsos 1
Laiwan Chan 1
Christophe Giraud-Carrier 1
Ling Liu 1
Huilei He 1
Hua Wang 1
Matthew Rattigan 1
Limin Yao 1
Huaimin Wang 1
Qiang You 1
Luke McDowell 1
Miao Tian 1
Qi Tian 1
Jennifer Neary 1
Olvi Mangasarian 1
Minoru Kanehisa 1
Kunta Chuang 1
Anthony Tung 1
Chris Clifton 1
Gianluigi Greco 1
Francesco Gullo 1
Guimei Liu 1
Henry Tan 1
Lorenzo De Stefani 1
Alessandro Epasto 1
Caetanotraina Jr 1
Oualid Boutemine 1
Yixuan Li 1
Victorjunqiu Wei 1
Irwin King 1
Sigal Sina 1
Sarit Kraus 1
Chris Ding 1
Lior Rokach 1
Dityan Yeung 1
Qingyan Yang 1
Patrick Haffner 1
Zhili Zhang 1
Ming Zhang 1
Shlomi Dolev 1
Fang Wang 1
Haixun Wang 1
Zhirui Hu 1
Dheeraj Kumar 1
Yao Wu 1
Dandan Qiao 1
Qiaozhu Mei 1
Siqi Shen 1
Xinran He 1
Lei Li 1
Zhongfei Zhang 1
Yunxin Zhao 1
Ming Shao 1
Yun Fu 1
Karl Pazdernik 1
Hong Huang 1
Loris Belcastro 1
Sebastian Moreno 1
Sergey Kirshner 1
Marco Bressan 1
Edward Toth 1
Jianzong Wang 1
Xiuyao Song 1
Takeshi Yamada 1
Jude Shavlik 1
Hui Ke 1
Tamara Kolda 1
Ying Wang 1
Yandong Liu 1
Suresh Iyengar 1
Jiawei Han 1
Zheng Wang 1
Johannes Schneider 1
Bin Cui 1
Juanzi Li 1
Xiaodan Song 1
Yasuhiro Fujiwara 1
Wei Wang 1
Scott Burton 1
John Gums 1
Ashwin Machanavajjhala 1
Ali Hemmatyar 1
Meng Jiang 1
Yi Zhen 1
Beilun Wang 1
Chihya Shen 1
Zhitao Wang 1
Erheng Zhong 1
Wei Fan 1
Edward Clarkson 1
Fuxin Li 1
Eder Carvalho 1
Jie Wang 1
Karthik Subbian 1
Galileo Namata 1
João Duarte 1
Yulan He 1
John Frenzel MD 1
Joshua Vogelstein 1
Hua Duan 1
Feng Chen 1
Eugenia Kontopoulou 1
Yin Zhang 1
Bingsheng Wang 1
ChienWei Chen 1
Weiyin Loh 1
John Salerno 1
Nitin Kumar 1
Flip Korn 1
Chris Ding 1
Han Liu 1
Christos Boutsidis 1
Giacomo Berardi 1
Xiaotong Zhang 1
Kathleen Carley 1
Ke Wang 1
Jing Zhang 1
Benoît Dumoulin 1
Qian Sun 1
Lorenzo Severini 1
Ou Wu 1
Lei Ma 1
Xing Yong 1
T Murali 1
Kiyoko Aoki-Kinoshita 1
Ravi Janardan 1
Sudhir Kumar 1
Sibel Adalı 1
Xiaohui Lu 1
Domenico Saccà 1
Francesco Lupia 1
Nima Mirbakhsh 1
Antti Ukkonen 1
Xindong Wu 1
Lei Xie 1
Hyunah Song 1
Haifeng Chen 1
Xiang Zhang 1
Hao Ye 1
Nenghai Yu 1
Peter Triantafillou 1
Manasi Patwardhan 1
Divya Pandove 1
Tiancheng Lou 1
Guna Seetharaman 1
Riccardo Ortale 1
David Bindel 1
Cheng Long 1
Shujian Huang 1
Zhanpeng Fang 1
Jing Peng 1
Ming Li 1
Jeffrey Erman 1
Bin Liu 1
Antonio Ortega 1
Chris Volinsky 1
Lei Zhang 1
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Hongxia Yang 1
Jiongqian Liang 1
Xuhui Li 1
Yanchun Zhang 1
Ravi Kumar 1
Yue Ning 1
Timothy La Fond 1
Noman Mohammed 1
Chao Liu 1
Johannes Gehrke 1
Collin Stultz 1
Boleslaw Szymanski 1
Maguelonne Teisseire 1
Zhishan Guo 1
Yunsing Koh 1
Silei Xu 1
Bo Liu 1
Leonid Hrebien 1
Pei Yang 1
Li Li 1
Denian Yang 1
Christo Wilson 1
Ben Zhao 1
Crystal Chen 1
Susan Frankenstein 1
Jaegul Choo 1
Feng Tian 1
Stephen Brooks 1
Evangelos Milios 1
Juanzi Li 1
Javier Barria 1
David Dominguez-Sal 1
Danai Koutra 1
Seunghee Bae 1
Can Chen 1
Chun Li 1
Jianyong Wang 1
Feitony Liu 1
Nick Duffield 1
Tanmoy Chakraborty 1
David Leake 1
Chenguang Wang 1
Zhoujun Li 1
Neilzhenqiang Gong 1
Yi Chang 1
Qiang Wei 1
John Hutchins 1
Taneli Mielikäinen 1
Lei Zou 1
Luming Zhang 1
Jian Wang 1
Manos Papagelis 1
Ruud Van De Bovenkamp 1
Clyde Giles 1
Wei Peng 1
Dale Schuurmans 1
Dimitrios Mavroeidis 1
Guangyong Chen 1
Sharad Mehrotra 1
Jiahui Zhu 1
Richard Ma 1
Fabrizio Marozzo 1
Domenico Talia 1
Hongfu Liu 1
Yun Fu 1
Tania Ghosh 1
Abhisek Kundu 1
Erik Saule 1
Tao Ku 1
Yunhong Hu 1
Paolo Boldi 1
Lini Thomas 1
Joydeep Ghosh 1
Kaiyuan Zhang 1
Carlos Ordonez 1
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James Cheng 1
Yixin Chen 1
Xuanhong Dang 1
Dimitrios Gunopulos 1
Li Zheng 1
Peter Christen 1
Daniel Dunlavy 1
Christos Doulkeridis 1
Andreas Krause 1
Hsiangfu Yu 1
Mohsen Bayati 1
Peilin Zhao 1
Sunil Vadera 1
S Upham 1
Kasim Candan 1
Steven Skiena 1
Hiroshi Motoda 1
Aditya Parameswaran 1
Binbin Lin 1
Thomas Porta 1
Hongzhi Yin 1
Pedro Vaz De Melo 1
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Michael Houle 1
Dora Erdős 1
Ricardo Campello 1
Qiang Qu 1
Shuhui Wang 1
Raymond Wong 1
Ada Fu 1
Shumo Chu 1
Bertil Schmidt 1
Yi Yang 1
Quanzeng You 1
Tao Mei 1
Kosuke Hashimoto 1
Jieping Ye 1
Muthuramakrishnan Venkitasubramaniam 1
Zeeshan Syed 1
Moshe Kam 1
Licong Cui 1
Xiaofeng Zhu 1
Victor Lee 1
Robert Kleinberg 1
Zhi Yang 1
Yafei Dai 1
Haesun Park 1
Charles Stolper 1
Paul Thompson 1
Edgar Treviño 1
Jinpeng Wang 1
Arnau Prat-Pérez 1
Josep Larriba-Pey 1
Risa Myers 1
Qingtian Zeng 1
Bill Howe 1
Nicholasjing Yuan 1
Yu Yang 1
Ahmet Sarıyüce 1
Changtien Lu 1
Sen Wang 1
Chong Peng 1
Kamalakar Karlapalem 1
Neil Smalheiser 1
Maria Halkidi 1
Tengfei Bao 1
Brook Wu 1
Peer Kröger 1
Céline Robardet 1
Jean Boulicaut 1
Pradeep Tamma 1
Ji Liu 1
Manuel Gomez-Rodriguez 1
David Gleich 1
Steven Hoi 1
Lyle Ungar 1
Franco Turini 1
Xiao Jiang 1
Sethuraman Panchanathan 1
Abdullah Mueen 1
Luigi Pontieri 1
Nobuhisa Ueda 1
Daxin Jiang 1
Jaideep Vaidya 1
Bingrong Lin 1
Francesco Bonchi 1
Tharam Dillon 1
Ashton Anderson 1
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Qing He 1
Kai Zhang 1
Yue Wu 1
Christos Anagnostopoulos 1
Kun He 1
Fengyu Qiu 1
Carla Chiasserini 1
Fazli Can 1
Bryan Perozzi 1
Edward Chang 1
Jie Tang 1
Haiqin Yang 1
Aparna Varde 1
Yu Jin 1
Ana Maguitman 1
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Foto Afrati 1
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Xiaofang Zhou 1
Xindong Wu 1
Fangtao Li 1
Junjie Wu 1
Padhraic Smyth 1
Claudia Plant 1
Jiayu Pan 1
Cheng Zeng 1
Atreya Srivathsan 1
Tong Sun 1
Ashish Verma 1
Nitesh Chawla 1
Yasser Altowim 1
Dmitri Kalashnikov 1
Pei Yang 1
Dmitry Pavlov 1
Piotr Indyk 1
Christopher Carothers 1
Anne Laurent 1
Maryam Tahani 1
Ying Wei 1
Luan Tang 1
Yizhou Sun 1
Xiaofei He 1
Comandur Seshadhri 1
Quanquan Gu 1
Xintao Wu 1
Jörg Sander 1
Siyuan Liu 1
David Jensen 1
James Cheng 1
Shachar Kaufman 1
Ori Stitelman 1
Leland Wilkinson 1
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Yu Zheng 1
Saurav Sahay 1
Glenn Fung 1
José Balcázar 1
Hockhee Ang 1
Weekeong Ng 1
Mengling Feng 1
Zengjian Hu 1
Boaz Ben-Moshe 1
Tao Mei 1
Essam Algizawy 1
Deb Roy 1
Raymond Wong 1
Xinyu Dai 1
Rui Xia 1
Tao Li 1
Jiajun Chen 1
Xiaowen Ding 1
Dino Ienco 1
Rosa Meo 1
Subhabrata Sen 1
Carlos Lorenzetti 1
Thomas Reichherzer 1
Dan Roth 1
Ephraim Korach 1
Jeffrey Ullman 1
Wenyuan Zhu 1
Kai Zheng 1
Zhongyuan Wang 1
Allon Percus 1
Xunhua Guo 1
Stephen North 1
Shantanu Godbole 1
Lei Shi 1
Yubao Wu 1
Fatma Bouali 1
Gilles Venturini 1
Maoyuan Sun 1
Rosane Minghim 1
Yang Yang 1
Muhammad Hameed 1
Yongli Hu 1
Srayan Datta 1
Eytan Adar 1
Eric Xing 1
Albert Bifet 1
Xiaoming Li 1
Josep Brunat 1
Martin Rosvall 1
Guoqing Chen 1
Xiaojun Chang* 1
Lina Yao 1
Zhao Kang 1
Xin Jin 1
Tao Yang 1
Polina Rozenshtein 1
Satyanarayana Valluri 1
Veerabhadran Baladandayuthapani 1
Giulio Rossetti 1
Ben Kao 1
David Cheung 1
Li Tu 1
Yanchi Liu 1
Songhua Xu 1
Jérémy Besson 1
Raghu Ramakrishnan 1
Timothy De Vries 1
Vasileios Kandylas 1
Salvatore Ruggieri 1
Ramana Kompella 1
Chengkai Li 1
Jiang Bian 1
Brandon Westover 1
Eamonn Keogh 1
Jing Zhang 1
Rodrigo Alves 1
Juhua Hu 1
Duygu Ucar 1
Mustafa Bilgic 1
Rezwan Ahmed 1
Alessandra Sala 1
Sheng Li 1
Rian Bahran 1
Yuxiao Dong 1
Min Peng 1
Christos Faloutsos 1
Sriparna Saha 1
Indrajit Banerjee 1
Fan Guo 1
John Guttag 1
Marc Plantevit 1
Sri Ravana 1
Shiqiang Yang 1
Zoran Obradović 1
Wangchien Lee 1
Tim Oates 1
Hanseung Lee 1
Chris North 1
Zhen Guo 1
Yashu Liu 1
Faming Lu 1
Ümit Çatalyürek 1
Xutong Liu 1
Yencheng Lu 1
Xue Li 1
Guodong Long 1
Jie Cheng 1
Jinlin Chen 1
Eduardo Hruschka 1
Hongliang Fei 1
Jun Huan 1
Carlos Garcia-Alvarado 1
Kevin Yip 1
Wei Zheng 1
Bin Zhou 1
Baoxing Huai 1
Hengshu Zhu 1
Nick Street 1
Pritam Gundecha 1
Alin Dobra 1
Ana Appel 1
Jeffreyxu Yu 1
Seungil Huh 1
Chojui Hsieh 1
Chihjen Lin 1
Andrew Mehler 1
Zheng Wang 1
Saharon Rosset 1
Wei Wei 1
Kun Liu 1
Duo Zhang 1
Claudia Perlich 1
Tuannhon Dang 1
Hoangvu Dang 1
Fen Xia 1
Linlin Zong 1
Yijuan Lu 1
Feng Liu 1
Yufeng Wang 1
Ernest Garcia 1
Shamkant Navathe 1
Muna Al-Razgan 1
Raymond Ng 1
Seekiong Ng 1
Rong Ge 1
Byronju Gao 1
Yunfei Lu 1
Wenwu Zhu 1
Ahmed El-Mahdy 1
Jeffreyxu Yu 1
Soroush Vosoughi 1
Vishal Kaushal 1
Michele Garetto 1
Wei Fan 1
Ron Eyal 1
Avi Rosenfeld 1
Asaf Shabtai 1
Shifeng Weng 1
Ruggero Pensa 1
Jun Yan 1
James BEZDEK 1
Marimuthu Palaniswami 1
Jayavardhana Gubbi 1
Alexander Ihler 1
Fernando Kuipers 1
Dick Epema 1
Linpeng Tang 1
Min Wang 1
Bo Long 1
Fengyuan Zhu 1
Xiaoming Fu 1
Deepak Ajwani 1
Patrick Nicholson 1
Xiuzhen Zhang 1
Alessandro Panconesi 1
Brandon Norick 1
Thanawin Rakthanmanon 1
Jesin Zakaria 1
Kedar Bellare 1
Ming Ji 1
Siddharth Gopal 1
Madhav Jha 1
Alice Leung 1
Renato Assunção 1
Sitaram Asur 1
Jerry Kiernan 1
Zhenxing Wang 1
Ravi Konuru 1
Anushka Anand 1
Pierluigi Crescenzi 1
Zijun Yao 1
Weiming Hu 1
Maoying Qiao 1
Wei Bian 1
Ying Jin 1
Hiroshi Mamitsuka 1
Edward Wild 1
Murat Kantarcıoğlu 1
Yicheng Tu 1
Dan Simovici 1
Hao Wang 1
Binay Bhattacharya 1
Lei Ying 1
Yu Shi 1
Tianyang Zhang 1
Shiqiang Yang 1
Agma Traina 1
Mostafa Mohsenvand 1
Shivani Goel 1
Kyle Kloster 1
Emili Leonardi 1
Hamed Bonab 1
Feifan Fan 1
Yuval Elovici 1
Ming Lin 1
Changshui Zhang 1
Sriram Srinivasan 1
Niloy Ganguly 1
Animesh Mukherjee 1
Sanjukta Bhowmick 1

Affiliation Paper Counts
Hong Kong Red Cross Blood Transfusion Service 1
University of Shizuoka 1
Osaka University 1
University of New South Wales 1
University of Kuwait 1
Anhui University 1
National University of Defense Technology China 1
National Technical University of Athens 1
University of Louisiana at Lafayette 1
Hong Kong Polytechnic University 1
University of Texas at Dallas 1
Lanzhou University 1
Korea University 1
INRIA Lorraine 1
Vilnius University 1
Zhejiang Wanli University 1
Syracuse University 1
University of Missouri-Columbia 1
University of Notre Dame 1
University of West Florida 1
National Chiao Tung University Taiwan 1
Iowa State University 1
University of Montpellier 1
Universite de Cergy-Pontoise 1
City University of New York 1
University at Buffalo, State University of New York 1
HP Labs 1
Air Force Research Laboratory 1
University of Ontario Institute of Technology 1
Norwegian University of Science and Technology 1
Lancaster University 1
Ryukoku University 1
Jerusalem College of Technology 1
Sandia National Laboratories, California 1
University of Toronto 1
IBM, India 1
South China University of Technology 1
South China Normal University 1
Yale University 1
California State University Fullerton 1
University of Michigan 1
Universidad Politecnica de Valencia 1
University of Auckland 1
University of Malaya 1
University of Connecticut 1
Waseda University 1
Queen's University Belfast 1
University of South Florida Tampa 1
Universite Lille 2 Droit et Sante 1
Curtin University of Technology, Perth 1
Aston University 1
Nokia USA 1
Sabanci University 1
eBay, Inc. 1
Macquarie University 1
BBN Technologies 1
University of Florence 1
University of Arizona 1
Universidad Adolfo Ibanez 1
Valley Laboratory 1
Research Organization of Information and Systems National Institute of Informatics 1
Claremont Graduate University 1
Washington University in St. Louis 1
Colorado School of Mines 1
Wright-Patterson AFB 1
State University of New York at Albany 1
Griffith University 1
Princeton University 1
University of Milan 1
Queens College, City University of New York 1
Indian Institute of Science, Bangalore 1
National Taiwan University of Science and Technology 1
University of Salford 1
MITRE Corporation 1
National Central University Taiwan 1
Harvard School of Engineering and Applied Sciences 1
Southwestern University 1
University of Iowa 1
Oracle Corporation 1
Twitter, Inc. 1
University of Rochester 1
US Naval Academy 1
Nanjing University of Aeronautics and Astronautics 1
University of New Mexico 1
Eli Lilly and Company 1
De Montfort University 1
University of Tsukuba 1
Air Force Research Laboratory Information Directorate 1
Siemens USA 1
University of Vermont 1
Oak Ridge National Laboratory 1
Ariel University Center of Samaria 1
Australian National University 1
Shenyang Institute of Automation Chinese Academy of Sciences 1
Amazon.com, Inc. 1
Wright State University 1
University of Arkansas - Fayetteville 1
RMIT University 1
Soochow University 1
Universite Claude Bernard Lyon 1 1
Temple University 1
Alexandria University 1
Georgia Tech Research Institute 1
Brigham and Women's Hospital 1
Umea University 1
Florida Atlantic University 1
Yuncheng University 1
University of Hawaii at Hilo 1
Radboud University Nijmegen 1
John F. Kennedy School of Government 1
Stevens Institute of Technology 1
Naval Research Laboratory 1
University of Gottingen 1
Saarland University 1
John Carroll University 1
Innopolis University 1
Institute of High Performance Computing, Singapore 2
Huazhong University of Science and Technology 2
Microsoft Research Asia 2
University of Glasgow 2
Nanjing University of Science and Technology 2
Johns Hopkins University 2
The University of North Carolina at Charlotte 2
University of Minnesota System 2
University of Massachusetts Dartmouth 2
Drexel University 2
Thapar University 2
University of Maryland, Baltimore County 2
University of Electronic Science and Technology of China 2
University of Quebec in Montreal 2
Indian Institute of Technology Patna 2
Institute for Systems and Computer Engineering of Porto 2
University of California, Berkeley 2
University of Athens 2
University of Kansas Lawrence 2
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 2
Swiss Federal Institute of Technology, Zurich 2
Montclair State University 2
University of Ottawa, Canada 2
Nokia Corporation 2
University of California, Davis 2
Polytechnic Institute of Turin 2
University of Tokyo 2
The University of Hong Kong 2
University of Nebraska at Omaha 2
Indiana University 2
University of California, Los Angeles 2
University of Virginia 2
Zhejiang University 2
Industrial Technology Research Institute of Taiwan 2
IBM Zurich Research Laboratory 2
South National University 2
Harvard University 2
Brigham Young University 2
Bilkent University 2
Missouri University of Science and Technology 2
Hefei University of Technology 2
University of Quebec in Outaouais 2
University of Houston 2
Victoria University Melbourne 2
Kent State University 2
Vishwakarma Institute of Technology 2
Istituto Di Calcolo E Reti Ad Alte Prestazioni, Rende 2
Hong Kong Baptist University 2
University of California, San Diego 2
Tel Aviv University 2
International Institute of Information Technology Hyderabad 3
Lawrence Livermore National Laboratory 3
The Chinese University of Hong Kong, Shenzhen 3
INSA Lyon 3
Shandong University of Science and Technology 3
Southern Illinois University at Carbondale 3
Institute of Automation Chinese Academy of Sciences 3
Nokia Bell Labs 3
The University of British Columbia 3
Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India 3
Xerox Corporation 3
University of Massachusetts Boston 3
University of Kentucky 3
Bar-Ilan University 3
Binghamton University State University of New York 3
Seoul National University 3
University of Alberta 3
University of Texas M. D. Anderson Cancer Center 3
Facebook, Inc. 3
University of Queensland 3
Indian Institute of Technology, Kharagpur 3
Academia Sinica Taiwan 3
Italian National Research Council 3
Qatar Computing Research institute 3
Universite Francois-Rabelais Tours 3
Imperial College London 3
University of Hildesheim 3
Los Alamos National Laboratory 3
University of Pennsylvania 3
Beijing University of Technology 3
Boston University 4
University Michigan Ann Arbor 4
North Carolina State University 4
Universitat Politecnica de Catalunya 4
Dalhousie University 4
Yahoo Research Barcelona 4
IBM Research 4
Johannes Gutenberg University Mainz 4
Athens University of Economics and Business 4
Microsoft Corporation 4
Monash University 4
University of Antwerp 4
University of Washington, Seattle 4
Northeastern University 4
AT&T Inc. 4
University of Pisa 4
The University of Western Ontario 4
Emory University 4
Brown University 4
Shanghai Jiaotong University 4
Beihang University 4
Institute for Infocomm Research, A-Star, Singapore 4
Max Planck Institute for Informatics 4
University of Roma La Sapienza 4
Singapore Management University 4
Brookhaven National Laboratory 4
Rutgers, The State University of New Jersey 5
The University of North Carolina at Chapel Hill 5
Rice University 5
University of Texas at San Antonio 5
Case Western Reserve University 5
George Mason University 5
Chinese Academy of Sciences 5
University of Southern California 5
University of Southern California, Information Sciences Institute 5
Sandia National Laboratories, New Mexico 5
New Jersey Institute of Technology 5
The University of Sydney 5
Delft University of Technology 6
University of Massachusetts Amherst 6
Dalian University of Technology 6
AT&T Laboratories Florham Park 6
Ludwig Maximilian University of Munich 6
Kyoto University 6
National University of Singapore 6
University of California, Santa Barbara 6
University of California, Irvine 6
Yahoo Inc. 6
University of Turin 6
University of Minnesota Twin Cities 6
Nippon Telegraph and Telephone Corporation 6
Federal University of Minas Gerais 7
University of Wisconsin Madison 7
University of Florida 7
Wuhan University 7
University of Maryland 7
University of California, Riverside 7
Renmin University of China 7
Massachusetts Institute of Technology 7
Ben-Gurion University of the Negev 7
Google Inc. 8
Xi'an Jiaotong University 8
University of Texas at Austin 8
Rutgers University-Newark Campus 8
Pennsylvania State University 8
Nanyang Technological University 8
Oregon State University 9
Microsoft Research 9
Purdue University 9
Yahoo Research Labs 9
University of Sao Paulo 9
Aalto University 9
Ohio State University 9
Florida International University 10
National Taiwan University 10
University of Melbourne 10
University of Illinois at Chicago 10
Peking University 10
IBM Thomas J. Watson Research Center 10
Stony Brook University 10
Stanford University 10
Sharif University of Technology 11
Rensselaer Polytechnic Institute 11
Georgia Institute of Technology 12
University of Science and Technology of China 13
Nanjing University 13
Hong Kong University of Science and Technology 14
University of Texas at Arlington 15
Northwestern Polytechnical University China 16
Cornell University 16
University of Calabria 16
University of Technology Sydney 16
Simon Fraser University 17
Virginia Tech 18
NEC Laboratories America, Inc. 19
University of Illinois at Urbana-Champaign 19
Chinese University of Hong Kong 24
Tsinghua University 37
Carnegie Mellon University 44
Arizona State University 50

ACM Transactions on Knowledge Discovery from Data (TKDD)
Archive


2018
Volume 12 Issue 4, June 2018  Issue-in-Progress
Volume 20 Issue 3, April 2018
Volume 12 Issue 2, March 2018 Survey Papers and Regular Papers
Volume 12 Issue 1, February 2018 Special Issue (IDEA) and Regular Papers

2017
Volume 11 Issue 4, August 2017 Special Issue on KDD 2016 and Regular Papers
Volume 11 Issue 3, April 2017

2016
Volume 11 Issue 2, December 2016
Volume 11 Issue 1, August 2016
Volume 10 Issue 4, July 2016 Special Issue on SIGKDD 2014, Special Issue on BIGCHAT and Regular Papers
Volume 10 Issue 3, February 2016

2015
Volume 10 Issue 2, October 2015
Volume 10 Issue 1, July 2015
Volume 9 Issue 4, June 2015
Volume 9 Issue 3, April 2015 TKDD Special Issue (SIGKDD'13)

2014
Volume 9 Issue 2, November 2014
Volume 9 Issue 1, October 2014
Volume 8 Issue 4, October 2014
Volume 8 Issue 3, June 2014
Volume 8 Issue 2, June 2014
Volume 8 Issue 1, February 2014 Casin special issue

2013
Volume 7 Issue 4, November 2013
Volume 7 Issue 3, September 2013 Special Issue on ACM SIGKDD 2012
Volume 7 Issue 2, July 2013
Volume 7 Issue 1, March 2013

2012
Volume 6 Issue 4, December 2012 Special Issue on the Best of SIGKDD 2011
Volume 6 Issue 3, October 2012
Volume 6 Issue 2, July 2012
Volume 6 Issue 1, March 2012
Volume 5 Issue 4, February 2012

2011
Volume 5 Issue 3, August 2011
Volume 5 Issue 2, February 2011

2010
Volume 5 Issue 1, December 2010
Volume 4 Issue 3, October 2010
Volume 4 Issue 4, October 2010
Volume 4 Issue 2, May 2010
Volume 4 Issue 1, January 2010

2009
Volume 3 Issue 4, November 2009
Volume 3 Issue 3, July 2009
Volume 3 Issue 2, April 2009
Volume 3 Issue 1, March 2009
Volume 2 Issue 4, January 2009

2008
Volume 2 Issue 3, October 2008
Volume 2 Issue 2, July 2008
Volume 2 Issue 1, March 2008
Volume 1 Issue 4, January 2008

2007
Volume 1 Issue 3, December 2007
Volume 1 Issue 2, August 2007
Volume 1 Issue 1, March 2007
 
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