ACM Transactions on

Knowledge Discovery from Data (TKDD)

Latest Articles

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.

Forthcoming Articles
Exploiting User Posts for Web Document Summarization

User posts such as comments or tweets of a Web document provide additional valuable information to enrich the content of this document. When creating user posts, readers tend to borrow salient words or phrases in sentences. is can be considered as word variation. is paper proposes a framework which models the word variation aspect from readers to enhance the summarization of Web documents. In order to do that, the summarization was presented in two steps: scoring and selection. In the scoring step, the social information of a Web document such as user posts is exploited to model intra and inter relations in lexical and semantic levels. These relations are denoted in a mutual reinforcement similarity graph used to score each sentence and user post. A er scoring, the summarization is extracted by using a ranking approach or concept-based method formulated in the form of Integer Linear Programming. To confirm the efficiency of the framework, sentence and story highlight extraction tasks were taken as a case study on three datasets in two languages, English and Vietnamese. Experimental results show that: (i) the framework obtains improvements of ROUGE-scores compared to state-of-the-art baselines in social context summarization and (ii) the combination of intra and inter relations benefits the sentence extraction of single Web documents.

Designing Size Consistent Statistics for Accurate Anomaly Detection in Dynamic Networks

An important task in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they could be examples of malicious activity or network malfunction. Hypothesis testing using network statistics to summarize the behavior of the network provides a robust framework for the anomaly detection decision process. Unfortunately, choosing network statistics that are dependent on confounding factors like the total number of nodes or edges can lead to incorrect conclusions (e.g., false positives and false negatives). In this paper we describe the challenges that face anomaly detection in dynamic network streams regarding confounding factors. We also provide two solutions to avoiding error due to confounding factors: the first is a randomization testing method that controls for confounding factors, and the second is a set of size-consistent network statistics which avoid confounding due to the most common factors, edge count and node count.

Continuous-Time User Modeling in Presence of Badges: A Probabilistic Approach

User modeling plays an important role in delivering customized web services to the users and improving their engagement. However, most user models in the literature do not explicitly consider the temporal behavior of users. More recently, continuous-time user modeling has gained considerable attention and many user behavior models have been proposed based on temporal point processes. However, typical point process based models often considered the impact of peer influence and content on the user participation and neglected other factors. Gamification elements, are among those factors that are neglected, while they have a strong impact on user participation in online services. In this paper, we propose interdependent multi-dimensional temporal point processes that capture the impact of badges on user participation besides the peer influence and content factors. We extend the proposed processes to model user actions over the community based question and answering websites, and propose an inference algorithm based on Variational-EM that can efficiently learn the model parameters. Extensive experiments on both synthetic and real data gathered from Stack Overflow show that our inference algorithm learns the parameters efficiently and the proposed method can better predict the user behavior compared to the alternatives.

iGRM: Improved Grey Relational Model and its Ensembles for Occupancy Sensing in Internet of Things Applications

Occupancy detection is one of the many applications of Building Automation Systems (BAS) or HVAC control systems especially with the rising demand of Internet of Things (IoT) services. This paper describes the fusion of data collected from sensors by exploiting their potential to sense occupancy in a room. For this purpose, a sensor test bed is deployed that includes four sensors measuring temperature, relative humidity, distance from the first obstacle and light along with a arduino micro-controller to validate our model. In addition, this paper proposes three algorithms for efficient fusion of the sensor data that is inspired by the Grey theory. An improved Grey Relational Model (iGRM) is proposed which acts as the base classifier for the other two alogirthms, namely, Grey Relational Model with Bagging (iGRM-BG) and Grey Relational Model with Boosting (iGRM-BS). Extensive simulation shows that very high accuracies (above 98\% and upto 100\%) is acquired with our proposed models. Among the three models, iGRM-BG is seen to perform better than the other two. The proposed algorithms are compared with some state-of-the-art models and is observed to outperform them all.

Social Network Monitoring for Bursty Cascade Detection

Social network services have become important and efficient platforms for users to share all kinds of information. The capability to monitor user-generated information and detect bursts from information diffusions in these social networks is bringing value to a wide range of real-life applications such as viral marketing. However, in reality, as a third party, there is always a cost for gathering information from each user or so-called social network sensor. The question then arises how to select a budgeted set of social network sensors to form the data stream for burst detection without compromising the detection performance. In this paper, we present a general sensor selection solution for different burst detection approaches. We formulate this problem as a constraint satisfaction problem which has high computational complexity. To reduce the computational cost, we first reduce most of the constraints by making use of the fact that bursty cascades are rare among the whole population. We then transform the problem into an LP (Linear Programming) problem. Furthermore, we use the sub-gradient method instead of the standard simplex method or interior-point method to solve the LP problem, which makes it possible for our solution to scale up to large social networks. Evaluating our solution on millions of real information cascades, we demonstrate both the effectiveness and efficiency of our approach.

Generating Realistic Synthetic Population Datasets

Modern studies of societal phenomena rely on the availability of large datasets capturing attributes and activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are necessary to study disease propagation and intervention measures before implementation. In social science, synthetic population datasets are needed to understand how policy decisions might affect preferences and behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic and procedural characteristics of patient records without violating confidentialities of individuals. To generate such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle to formalize a generative model such that in a statistically well-founded way we can optimally utilize given prior information about the data, and are unbiased otherwise. An efficient inference algorithm is designed to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and US census datasets, and demonstrate its feasibility using an epidemic simulation application.

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.

Multi-View Low-Rank Analysis with Applications to Outlier Detection

Detecting outliers or anomalies is a fundamental problem in various machine learning and data mining applications. Conventional outlier detection algorithms are mainly designed for single-view data. Nowadays, data can be easily collected from multiple views, and many learning tasks such as clustering and classification have benefited from multi-view data. However, outlier detection from multi-view data is still a very challenging problem, as the data in multiple views usually have more complicated distributions and exhibit inconsistent behaviors. To address this problem, we propose a multi-view low-rank analysis (MLRA) framework for outlier detection in this paper. MLRA pursuits outliers from a new perspective, robust data representation. It contains two major components. First, the cross-view low-rank coding is performed to reveal the intrinsic structures of data. In particular, we formulate a regularized rank-minimization problem which is solved by an efficient optimization algorithm. Second, the outliers are identified through an outlier score estimation procedure. Different from the existing multi-view outlier detection methods, MLRA is able to detect two different types of outliers from multiple views simultaneously. To this end, we design a criterion to estimate the outlier scores by analyzing the obtained representation coefficients. Moreover, we extend MLRA to tackle the multi-view group outlier detection problem. Extensive evaluations on seven UCI datasets, the MovieLens, the USPS-MNIST, and the WebKB datasets demonstrate that our approach outperforms several state-of-the-art outlier detection methods.

Consensus Guided Multi-View Clustering

In recent years, a large volume of techniques emerge in artificial intelligence field thanks to the easy accessibility of data captured from multiple sensors. These multi-view data provide much more rich information than traditional single-view data. Fusing heterogeneous information for certain tasks is a core part of multi-view learning, especially for multi-view clustering. Although many multi-view clustering algorithms have been proposed, most scholars focus on finding the common space of different views and unfortunately ignore the benefits from partition level by ensemble clustering. For ensemble clustering, however, there is no interaction between individual partitions from each view and the final consensus one. To fill the gap, we propose a Recursive Multi-View Clustering ($RMVC$) framework, which incorporates the generation of basic partitions from each view and fusion of consensus clustering in an interactive way, i.e., the consensus clustering guides the generation of basic partitions and high quality basic partitions positively contribute to the consensus clustering as well. We design a non-trivial optimization solution to formulate $RMVC$ into two iterative K-means clusterings by an approximate calculation. In addition, the generalization of $RMVC$ provides a rich feasibility to different scenarios, and the extension of $RMVC$ with incomplete multi-view clustering further validates the effectiveness for real-world applications. Extensive experiments demonstrate the advantages of $RMVC$ over other widely used multi-view clustering methods and its robustness to some important parameters and incomplete multi-view data.

Mining Graphlet Counts in Online Social Networks

Counting subgraphs is a fundamental analysis task for online social networks (OSNs). Given the sheer size and restricted access of online social network data, efficient computation of subgraph counts is highly challenging. Although a number of algorithms have been proposed to estimate the relative counts of subgraphs in OSNs with restricted access, there are only few works which try to solve a more general problem, i.e., counting subgraph frequencies. In this paper, we propose an efficient random walk-based framework to estimate the subgraph counts. Our framework generates samples by leveraging consecutive steps of the random walk as well as by observing neighbors of visited nodes. Using the importance sampling technique, we derive unbiased estimators of the subgraph counts. To make better use of the degree information of visited nodes, we also design improved estimators, which increases the accuracy of the estimation with no additional cost. We conduct extensive experimental evaluation on real-world OSNs to confirm our theoretical claims. The experiment results show that our estimators are unbiased, accurate, efficient and better than the state-of-the-art algorithms. For the Weibo graph with more than 58 million nodes, our method produces estimate of triangle count with an error less than 5% using only 20 thousands sampled nodes. Detailed comparison with the state-of-the-art methods demonstrates that our algorithm is 2 to 10 times more accurate.

Comparison of ontology alignment systems across single matching task via the McNemars test

Ontology alignment is widely used to find the correspondences between different ontologies in diverse fields. After discovering the alignment by methods, several performance scores are available to evaluate them. The scores require the produced alignment by a method and the reference alignment containing the underlying actual correspondences of the given ontologies. The current trend in alignment evaluation is to put forward a new score and to compare various alignments by juxtaposing their performance scores. However, it is substantially provocative to select one performance score among others for comparison. On top of that, claiming if one method has a better performance than one another can not be substantiated by solely comparing the scores. In this paper, we propose the statistical procedures which enable us to theoretically favor one method over one another. The McNemar test is considered as a reliable and suitable means for comparing two ontology alignment methods over one matching task. The test applies to a $2 \times 2$ contingency table which can be constructed in two different ways based on the alignments, each of which has their own merits/pitfalls. The ways of the contingency table construction and various apposite statistics from the McNemar test are elaborated in minute detail. In the case of having more than two alignment methods for comparison, the family-wise error rate is expected to happen. Thus, the ways of preventing such an error are also discussed. A directed graph visualizes the outcome of the McNemar test in the presence of multiple alignment methods. From this graph, it is understood if one method is better than one another or if their differences are imperceptible. Our investigation on the methods participated in the anatomy track of OAEI 2016 demonstrates that AML and CroMatcher are the top two and DKP-AOM and Alin are the bottom two ones.

Twitter Geolocation: A Hybrid Approach

Geotagging Twitter messages is an important tool for event detection and enrichment. Despite the availability of both social media content and user network information, these two features are generally utilized separately in the methodology. In this paper, we create a hybrid method that uses Twitter content and network information jointly as model features. We use Gaussian mixture models to map the raw spatial distribution of the model features to a predicted field. This approach is scalable to large data sets and provides a natural representation of model confidence. Our method is tested against other approaches and we achieve greater prediction accuracy. The model also improves both precision and coverage.

GT^: Detecting Temporal Changes in Group Stochastic Processes

Given a portfolio of stocks or a series of frames in a video how do we detect significant changes in a group of values for real-time applications? In this paper, we formalize the problem of sequentially detecting temporal changes in a group of stochastic processes. As a solution to this general problem, we propose the group temporal change (GT”) algorithm, a simple yet effective technique for the sequential detection of significant changes in a variety of statistical properties of a group over time. Due to the flexible framework of the GT” algorithm, a domain expert is able to select one or more statistical properties that they are interested in monitoring. The usefulness of our proposed algorithm is also demonstrated against state-of-the-art techniques on synthetically generated data as well as on two real-world applications; a portfolio of healthcare stocks over a twenty year period and a video monitoring the activity of our Sun.

Exploring Multiobjective Optimization for Multi-view Clustering

We present a new multi-view clustering approach based on multiobjective optimization. In contrast to existing clustering algorithms based on multiobjective optimization, it is generally applicable to data represented by two or more views and does not require specifying the number of clusters a priori. The approach builds upon the search capability of a multiobjective simulated annealing based technique, AMOSA, as the underlying optimization technique. In the first version of the proposed approach, an internal cluster validity index is used to assess the quality of different partitionings obtained using different views. A new way of checking the compatibility of these different partitionings is also proposed and this is used as another objective function. A new encoding strategy and some new mutation operators are introduced. Finally, a new way of computing a consensus partitioning from multiple individual partitions obtained on multiple views is proposed. In the second and third approaches, some multiobjective based ensemble clustering techniques are proposed to combine the outputs of different simple clustering approaches. The efficacy of the proposed clustering methods is shown for partitioning several real-world data sets having multiple views. To show the practical usefulness of the method, we present results on web-search result clustering, where the task is to find a suitable partitioning of web snippets.

Motif Counting Beyond Five Nodes

Counting graphlets is a well-studied problem in graph mining and social network analysis. Recently, several papers explored very simple and natural approaches based on Monte Carlo sampling of Markov Chains (MC), and reported encouraging results. We show, perhaps surprisingly, that this approach is outperformed by a carefully engineered version of color coding (CC)~\cite{Alon&1995}, a sophisticated algorithmic technique that we extend to the case of graphlet sampling and for which we prove strong statistical guarantees. Our computational experiments on graphs with millions of nodes show CC to be more efficient than MC. Furthermore, we formally show that the mixing time of the MC approach is too high in general, even when the input graph has high conductance. All this comes at a price however. While MC is very efficient in terms of space, CC's memory requirements become demanding when the size of the input graph and that of the graphlets grow. And yet, our experiments show that a careful implementation of CC can push the limits of the state of the art, both in terms of the size of the input graph and of that of the graphlets.

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.

Behavior2Vec: Generating Distributed Representations of Users' Behaviors on Products for Recommender Systems

We propose to generate the distributed representations of users' viewing and purchasing behaviors on an EC website. By leveraging on the cosine distance between the distributed representations, we can predict a user's next clicking or purchasing item more precisely, compared to several baseline methods. Perhaps more importantly, we found that the distributed representations may help discover interesting analogies among the products. We may utilize such analogies to explain how two products are related, and eventually apply different recommendation strategy under different scenarios.

Mining Event-oriented Topics in Microblog Stream with Unsupervised Multi-view Hierarchical Embedding

This paper presents an unsupervised multi-view hierarchical embedding (UMHE) framework to sufficiently reveal the intrinsic topical knowledge in social events. Mining topics are highly related to such events as it can provide explicit descriptions of what have happened in social community. In many real-world cases, however, it is difficult to include all attributes of microblogs, more often, textual aspects only are available. Traditional topic modeling methods are failed to generate event-oriented topics with the textual aspects, since the inherent relations between topics are often overlooked in these methods. Meanwhile, the metrics in original word vocabulary space might not effectively capture semantic distances. The UMHE framework overcomes the severe information deficiency and poor feature representation. The UMHE first develops a multi-view Bayesian rose tree to preliminarily generate prior knowledge for latent topics and their relations. With such prior knowledge, we design an unsupervised translation-based hierarchical embedding method to make a better representation of these latent topics. By applying self-adaptive spectral clustering on the embedding space and the original space concomitantly, we eventually extract event-oriented topics in word distributions to express social events. Our framework is purely data-driven and unsupervised, without any external knowledge. Experimental results on Twitter and Sina Weibo datasets demonstrate that the UMHE framework can construct hierarchical structure with high fitness, but also yield topic embeddings with salient semantics, therefore, it can derive event-oriented topics with meaningful descriptions.

ProgressER: Adaptive Progressive Approach to Relational Entity Resolution

Entity resolution (ER) is the process of identifying which entities in a dataset refer to the same real-world object. In relational ER, the dataset consists of multiple entity-sets and relationships among them. Such relationships cause the resolution of some entities to influence the resolution of other entities. For instance, consider a relational dataset that consists of a set of research paper entities and a set of venue entities. In such a dataset, deciding that two research papers are the same may trigger the fact that their venues are also the same. This article proposes a progressive approach to relational entity resolution that aims to produce the highest quality result given a constraint on the resolution budget, specified by the user. Such a progressive approach is useful for many emerging analytical applications that require low latency response (and thus can not tolerate delays caused by cleaning the entire dataset) and/or in situations where the underlying resources are constrained or costly to use. To maximize the quality of the result, our proposed approach follows an adaptive strategy that periodically monitors and reassesses the resolution progress to determine which parts of the dataset should be resolved next and how they should be resolved. The comprehensive empirical evaluation of the proposed approach demonstrates its significant advantage in terms of progressiveness over the traditional ER techniques for the given problem settings.

Tied Kronecker Product Graph Models to Capture Variance in Network Populations

Much of the past work on mining and modeling networks has focused on understanding the observed prop- erties of single example graphs. However, in many real-life applications it is important to characterize the structure of populations of graphs. In this work, we start analyzing the distributional properties of prob- abilistic generative graph models (PGGMs) in network populations. PGGMs are statistical methods that model the network distribution and match common characteristics of real world networks. Specifically, we proof that most PGGMs cannot represent the natural variability in graph properties observed across multi- ple networks, because their edge generation process assume independency among edges. Then, we propose the mixed Kronecker Product Graph Model (mKPGM), a scalable generalization of KPGMs that uses tied parameters to increase the variance of the model, while preserving the expectation. We compare mKPGM to several other graph models. The results show that learned mKPGMs accurately represent the character- istics of real-world networks, while still providing natural variability in the network statistics.

Function-on-Function Regression with Mode-Sparsity Regularization

Functional data is ubiquitous in many domains such as healthcare, social media, manufacturing process, sensor networks, etc. Functional data analysis involves the analysis of data which is treated as infinite-dimensional continuous functions rather than discrete, finite-dimensional vectors. In this paper, we propose a novel function-on-function regression model based on mode-sparsity regularization. The main idea is to represent the regression coefficient function between predictor and response as the double expansion of basis functions, and then use mode-sparsity constraint to automatically filter out irrelevant basis functions for both predictors and responses. The proposed approach is further extended to the tensor version to accommodate multiple functional predictors. While allowing the dimensionality of the regression weight tensor to be relatively large, the mode-sparsity regularized model facilitates the multi-way shrinking of basis functions for each mode. Meanwhile, it covers a wide spectrum of sparse models for function-on-function regression. The resulting optimization problem is challenging due to the non-smooth property of the mode-sparsity. We develop an efficient algorithm to solve the problem, which works in an iterative update fashion, and converges to the global optimum. Furthermore, we analyze the generalization performance of the proposed method and derive the error bound on the consistency between the recovered function and the underlying true function. The effectiveness of the proposed approach is verified on benchmark functional data sets in various domains.

Evasion-Robust Classification on Binary Domains

The success of classification learning has led to numerous attempts to apply it in adversarial settings such as spam and malware detection. The core challenge in this class of applications is that adversaries are not static, but make a deliberate effort to evade the classifiers. We investigate both the problem of modeling the objectives of such adversaries, as well as the algorithmic problem of accounting for rational, objective-driven adversaries. We first present a general approach based on mixed-integer linear programming (MILP) with constraint generation. This approach is the first to compute an optimal solution to adversarial loss minimization for two general classes of adversarial evasion models in the context of binary feature spaces. To further improve scalability and significantly generalize the scope of the MILP-based method, we propose a principled iterative retraining framework, which can be used with arbitrary classifiers and essentially arbitrary attack models. We show that the retraining approach, when it converges, minimizes an upper bound on adversarial loss. Extensive experiments demonstrate that the mixed-integer programming approach significantly outperforms several state-of-the-art adversarial learning alternatives. Moreover, the retraining framework performs nearly as well, but scales significantly better. Finally, we show that our approach is robust to misspecifications of the adversarial model.


Publication Years 2007-2018
Publication Count 330
Citation Count 3010
Available for Download 330
Downloads (6 weeks) 3748
Downloads (12 Months) 30361
Downloads (cumulative) 217748
Average downloads per article 660
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)
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 Yanbin 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
Jian Pei 5
John Lui 5
Aristides Gionis 5
Tao Li 5
John Hopcroft 4
Philip YU 4
Zhiwen Yu 4
Hanghang Tong 4
Zhihua Zhou 4
Shenghuo Zhu 4
Heng Huang 4
Feiping Nie 4
Bin Guo 4
Huan Liu 4
Hong Cheng 4
Christopher Jermaine 4
Nikolaj Tatti 3
Guofei Jiang 3
Lise Getoor 3
Jure Leskovec 3
Malik Magdon-Ismail 3
Mingsyan Chen 3
Xiaoli Fern 3
Enhong Chen 3
Qi Liu 3
Jilles Vreeken 3
Yun Chi 3
Evimaria Terzi 3
Yasushi Sakurai 3
Yihong Gong 3
Lei Tang 3
Srinivasan Parthasarathy 3
Jirong Wen 3
Dingding Wang 3
Fabio Fassetti 3
Chengqi Zhang 3
Fabrizio Angiulli 3
Vivekanand Gopalkrishnan 2
Jie Tang 2
U Kang 2
Eugene Agichtein 2
Christopher Leckie 2
Carlotta Domeniconi 2
Sanjay Ranka 2
Kui Yu 2
Jiliang Tang 2
Dantong Yu 2
Hong Xie 2
Charalampos Tsourakakis 2
Hamid Rabiee 2
Martin Ester 2
Hari Sundaram 2
Spiros Papadimitriou 2
Mohamed Bouguessa 2
Bryan Hooi 2
Joydeep Ghosh 2
Wei Fan 2
Dino Pedreschi 2
Eli Upfal 2
Charu Aggarwal 2
Neil Shah 2
Jon Kleinberg 2
Wei Wang 2
Naren Ramakrishnan 2
Kristina Lerman 2
Pinghui Wang 2
Hao Huang 2
Hong Qin 2
Kijung Shin 2
Yehuda Koren 2
Arnold Boedihardjo 2
Heikki Mannila 2
Panayiotis Tsaparas 2
Leman Akoglu 2
Chen Chen 2
Zhu Wang 2
Jianhui Chen 2
Yu Zhang 2
Arthur Zimek 2
Michalis Vazirgiannis 2
Yangqiu Song 2
Junzhou Zhao 2
Xiaohong Guan 2
Geoffrey Webb 2
Indrajit Bhattacharya 2
Panagis Magdalinos 2
Jin Huang 2
Xiao Yu 2
Qiang Yang 2
Wei Cheng 2
Peng Cui 2
Yanjie Fu 2
Charles Ling 2
Andrea Esuli 2
JiLei Tian 2
Ping Luo 2
B Prakash 2
Yuru Lin 2
Shinjae Yoo 2
Jie Tang 2
Petros Drineas 2
Ian Davidson 2
Antonella Guzzo 2
Steven Hoi 2
Jiawei Han 2
Ruoming Jin 2
Antônio Loureiro 2
Jiawei Han 2
Lei Chen 2
Daniel Kifer 2
Pauli Miettinen 2
Waynexin Zhao 2
Xiang Zhang 2
Alex Beutel 2
Fabrizio Sebastiani 2
Laks Lakshmanan 2
Yan Liu 2
Jimeng Sun 2
Don Towsley 2
Rita Chattopadhyay 2
Sucheta Soundarajan 2
Matteo Riondato 2
Maryam Ramezani 2
Yong Ge 2
Xianchao Zhang 2
Dacheng Tao 2
Belle Tseng 2
Wei Ding 2
Chong Peng 1
Neil Smalheiser 1
James Cheng 1
Shachar Kaufman 1
Ori Stitelman 1
Leland Wilkinson 1
José Balcázar 1
Weekeong Ng 1
Hockhee Ang 1
Mengling Feng 1
Xiao Jiang 1
Lyle Ungar 1
Franco Turini 1
Comandur Seshadhri 1
Luan Tang 1
Quanquan Gu 1
Xintao Wu 1
Nick Duffield 1
Chun Li 1
Jianyong Wang 1
Feitony Liu 1
Sanjay Chawla 1
Jinpeng Wang 1
Josep Larriba-Pey 1
Arnau Prat-Pérez 1
Risa Myers 1
Qingtian Zeng 1
Brian Gallagher 1
John Hutchins 1
Taneli Mielikäinen 1
Ji Liu 1
Manuel Gomez-Rodriguez 1
Sethuraman Panchanathan 1
Abdullah Mueen 1
Yizhou Sun 1
Xiaofei He 1
Muthuramakrishnan Venkitasubramaniam 1
Robert Kleinberg 1
Victor Lee 1
Zhi Yang 1
Charles Stolper 1
Yafei Dai 1
Haesun Park 1
Paul Thompson 1
Edgar Treviño 1
Moshe Kam 1
Wei Wang 1
ChienWei Chen 1
Weiyin Loh 1
Guna Seetharaman 1
Kathleen Carley 1
Yasuhiro Fujiwara 1
John Salerno 1
Nitin Kumar 1
Flip Korn 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
Ying Wang 1
Siqi Shen 1
Xinran He 1
Lei Li 1
Ke Wang 1
Chris Ding 1
Jing Zhang 1
Xiuyao Song 1
John Gums 1
Benoît Dumoulin 1
Hong Huang 1
Loris Belcastro 1
Yin Zhang 1
Zhongfei Zhang 1
Jude Shavlik 1
Yunxin Zhao 1
Feng Chen 1
Eugenia Kontopoulou 1
Qian Sun 1
Sibel Adalı 1
Xiaohui Lu 1
Domenico Saccà 1
Francesco Lupia 1
Nima Mirbakhsh 1
Antti Ukkonen 1
Xindong Wu 1
Zheng Wang 1
Johannes Schneider 1
Bin Cui 1
Juanzi Li 1
Jieping Ye 1
Xiaofeng Zhu 1
Licong Cui 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
Pierluigi Crescenzi 1
Zijun Yao 1
Weiming Hu 1
Maoying Qiao 1
Wei Bian 1
Ying Jin 1
Hiroshi Mamitsuka 1
Jerry Kiernan 1
Kevin Yip 1
Wei Zheng 1
Sitaram Asur 1
Zhenxing Wang 1
Carlos Lorenzetti 1
Thomas Reichherzer 1
Jeffrey Ullman 1
Dan Roth 1
Ephraim Korach 1
Wenyuan Zhu 1
Kai Zheng 1
Zhongyuan Wang 1
Allon Percus 1
Xunhua Guo 1
Ravi Konuru 1
Baoxing Huai 1
Hengshu Zhu 1
Nick Street 1
Pritam Gundecha 1
Fan Guo 1
Edward Wild 1
Yuxiao Dong 1
Alessandra Sala 1
Murat Kantarcıoğlu 1
Christos Faloutsos 1
John Guttag 1
Marc Plantevit 1
Jinlin Chen 1
Shantanu Godbole 1
Alin Dobra 1
Binay Bhattacharya 1
Ümit Çatalyürek 1
Xutong Liu 1
Yencheng Lu 1
Xue Li 1
Guodong Long 1
Jie Cheng 1
Bin Zhou 1
Anushka Anand 1
Yicheng Tu 1
Dan Simovici 1
Hao Wang 1
Madhav Jha 1
Siddharth Gopal 1
Alice Leung 1
Renato Assunção 1
Subhabrata Sen 1
Dino Ienco 1
Rosa Meo 1
Eduardo Hruschka 1
Hongliang Fei 1
Jun Huan 1
Carlos Garcia-Alvarado 1
Ana Appel 1
Zhen Guo 1
Jeffreyxu Yu 1
Yashu Liu 1
Faming Lu 1
Andrew Mehler 1
Stephen North 1
Seungil Huh 1
Chihjen Lin 1
Chojui Hsieh 1
Zheng Wang 1
Thanawin Rakthanmanon 1
Jesin Zakaria 1
Kedar Bellare 1
Brandon Norick 1
Ming Ji 1
Yuval Elovici 1
Ming Lin 1
Changshui Zhang 1
Tim Oates 1
Hanseung Lee 1
Chris North 1
Sri Ravana 1
Shiqiang Yang 1
Zoran Obradović 1
Wangchien Lee 1
Lei Ying 1
Yu Shi 1
Shiqiang Yang 1
Tianyang Zhang 1
Agma Traina 1
Mostafa Mohsenvand 1
Shivani Goel 1
Kyle Kloster 1
Feifan Fan 1
Emili Leonardi 1
Hamed Bonab 1
Qinli Yang 1
Josif Grabocka 1
Xiang Li 1
David Aha 1
Nicolas Schilling 1
Richard Xu 1
Sougata Mukherjea 1
Ashwin Ram 1
Zhanpeng Fang 1
Yang Zhou 1
Xinjiang Lu 1
Dengyong Zhou 1
Ming Zhang 1
Biru Dai 1
Jing Peng 1
Divesh Srivastava 1
Hungleng Chen 1
Zhenjie Zhang 1
Sriram Srinivasan 1
Niloy Ganguly 1
Animesh Mukherjee 1
Sanjukta Bhowmick 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
Aisling Kelliher 1
Paul Castro 1
Lian Duan 1
Bruno Ribeiro 1
Siyuan Liu 1
Anon Plangprasopchok 1
Shengrui Wang 1
Patrick Hung 1
Ganesh Ramesh 1
A Patterson 1
Phengann Heng 1
Paolo Trunfio 1
Manolis Kellis 1
Carlos Castillo 1
Tianbing Xu 1
Sanmay Das 1
Amit Dhurandhar 1
Beechung Chen 1
Daniel Halperin 1
Jie Wang 1
Fedja Hadzic 1
Elizabeth Chang 1
Aminul Islam 1
Jian Cao 1
Shiyou Qian 1
Kamer Kaya 1
Quan Sheng 1
Maha Alabduljalil 1
Qiang Cheng 1
Li Wan 1
Weekeong Ng 1
Sethuraman Panchanathan 1
Yu Lei 1
Michael Mampaey 1
Limsoon Wong 1
Haojun Zhang 1
Shipeng Yu 1
Maria Sapino 1
Zhiting Hu 1
Pedro Melo 1
Yuan Jiang 1
Qinbao Song 1
Michele Coscia 1
Yi Wang 1
Charles Elkan 1
Jaideep Srivastava 1
João Gama 1
Carlos Guestrin 1
Naonori Ueda 1
Tomoharu Iwata 1
Qi Lou 1
Wei Fan 1
Xifeng Yan 1
Julian McAuley 1
Pavel Senin 1
Sunil Gandhi 1
Hannah Kim 1
John Stasko 1
Madelaine Daianu 1
Hao Wu 1
Esther Galbrun 1
Junbin Gao 1
Yanfeng Sun 1
Baocai Yin 1
Feiyu Xiong 1
Fei Wang 1
Shiqiang Tao 1
Guoqiang Zhang 1
Jingchao Ni 1
Alceu Costa 1
Yihan Wang 1
Rinkl Rani 1
David Bindel 1
Cheng Long 1
Shujian Huang 1
Riccardo Ortale 1
Bertil Schmidt 1
Yi Yang 1
Quanzeng You 1
Tao Mei 1
Kosuke Hashimoto 1
Nobuhisa Ueda 1
Jie Tang 1
Haiqin Yang 1
Aparna Varde 1
Ricardo Campello 1
Qiang Qu 1
Shuhui Wang 1
Pedro Vaz De Melo 1
Jeffrey Chan 1
Michael Houle 1
Dimitrios Gunopulos 1
Daxin Jiang 1
Bin Liu 1
Antonio Ortega 1
Mohsen Bayati 1
Peilin Zhao 1
Lei Zhang 1
Raymond Wong 1
Ada Fu 1
Li Zheng 1
Noman Mohammed 1
Chao Liu 1
Jaideep Vaidya 1
Hongxia Yang 1
Jiongqian Liang 1
Collin Stultz 1
Boleslaw Szymanski 1
Maguelonne Teisseire 1
Lini Thomas 1
Paolo Boldi 1
Sachindra Joshi 1
Seunghee Bae 1
Tharam Dillon 1
Can Chen 1
Erik Saule 1
Tao Ku 1
Yunhong Hu 1
Abhisek Kundu 1
Yixin Chen 1
Xuanhong Dang 1
Shumo Chu 1
Luigi Pontieri 1
Bingrong Lin 1
Francesco Bonchi 1
Sunil Vadera 1
Kasim Candan 1
S Upham 1
Thomas Porta 1
Hongzhi Yin 1
Jeffrey Erman 1
Ming Li 1
Dora Erdős 1
Kaiyuan Zhang 1
Joydeep Ghosh 1
Carlos Ordonez 1
Fosca Giannotti 1
James Cheng 1
Peter Christen 1
Daniel Dunlavy 1
Christos Doulkeridis 1
David Dominguez-Sal 1
Danai Koutra 1
Hiroshi Motoda 1
Steven Skiena 1
Chris Volinsky 1
Andreas Krause 1
Hsiangfu Yu 1
Aditya Parameswaran 1
Binbin Lin 1
Johannes Gehrke 1
Christo Wilson 1
Crystal Chen 1
Susan Frankenstein 1
Jaegul Choo 1
Ben Zhao 1
Feng Tian 1
Stephen Brooks 1
Evangelos Milios 1
Javier Barria 1
Juanzi Li 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
Ashton Anderson 1
Sendhil Mullainathan 1
Qing He 1
Kai Zhang 1
Yue Wu 1
Christos Anagnostopoulos 1
Kun He 1
Fengyu Qiu 1
Edward Chang 1
Bryan Perozzi 1
Carla Chiasserini 1
Fazli Can 1
Hoangvu Dang 1
Linlin Zong 1
Fen Xia 1
Feng Liu 1
Yijuan Lu 1
Yufeng Wang 1
Ernest Garcia 1
Shamkant Navathe 1
Duygu Ucar 1
Mustafa Bilgic 1
Ben Kao 1
David Cheung 1
Wei Fan 1
Rezwan Ahmed 1
Wei Wei 1
Muna Al-Razgan 1
Filippo Menczer 1
Ana Maguitman 1
Foto Afrati 1
Rómer Rosales 1
Xiaofang Zhou 1
Xindong Wu 1
Fangtao Li 1
Junjie Wu 1
Cheng Zeng 1
Atreya Srivathsan 1
Tong Sun 1
Yanchi Liu 1
Songhua Xu 1
Kun Liu 1
Duo Zhang 1
Raymond Ng 1
Nitesh Chawla 1
Dmitry Pavlov 1
Piotr Indyk 1
Christopher Carothers 1
Anne Laurent 1
Satyanarayana Valluri 1
Ashish Verma 1
Jérémy Besson 1
Raghu Ramakrishnan 1
Byronju Gao 1
Rong Ge 1
Martin Rosvall 1
Guoqing Chen 1
Xiaojun Chang* 1
Lina Yao 1
Zhao Kang 1
Xin Jin 1
Tao Yang 1
Li Tu 1
Saharon Rosset 1
Claudia Perlich 1
Polina Rozenshtein 1
Tuannhon Dang 1
Seekiong Ng 1
Ramana Kompella 1
Chengkai Li 1
Vasileios Kandylas 1
Salvatore Ruggieri 1
Jing Zhang 1
Rodrigo Alves 1
Juhua Hu 1
Yu Jin 1
Veerabhadran Baladandayuthapani 1
Giulio Rossetti 1
Timothy De Vries 1
Eric Xing 1
Albert Bifet 1
Xiaoming Li 1
Josep Brunat 1
Jiang Bian 1
Padhraic Smyth 1
Claudia Plant 1
Jiayu Pan 1
Brandon Westover 1
Eamonn Keogh 1
Ron Eyal 1
Avi Rosenfeld 1
Asaf Shabtai 1
Shifeng Weng 1
Fatma Bouali 1
Gilles Venturini 1
Lei Shi 1
Maoyuan Sun 1
Rosane Minghim 1
Yang Yang 1
Muhammad Hameed 1
Yongli Hu 1
Srayan Datta 1
Eytan Adar 1
Maryam Tahani 1
Ying Wei 1
Yubao Wu 1
Yunfei Lu 1
Wenwu Zhu 1
Ahmed El-Mahdy 1
Jeffreyxu Yu 1
Soroush Vosoughi 1
Vishal Kaushal 1
Michele Garetto 1
Junming Shao 1
Yllka Velaj 1
Xiaojun Chang 1
Lars Schmidt-Thieme 1
Michael Lyu 1
Dityan Yeung 1
Jilei Tian 1
Davoud Moulavi 1
Koji Hino 1
Xiang Zhang 1
Jenwei Huang 1
Evangelos Papalexakis 1
Nicholas Sidiropoulos 1
George Karypis 1
Masaru Kitsuregawa 1
James Bailey 1
Jianping Zhang 1
Graham Cormode 1
Manas Somaiya 1
Jun Yan 1
Marimuthu Palaniswami 1
James Bezdek 1
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Bin Li 1
Fernando Kuipers 1
Dick Epema 1
Linpeng Tang 1
Min Wang 1
Marc Maier 1
Lionel Ni 1
MingXi Wu 1
Benjamin Fung 1
Xiaoming Fu 1
Ye Chen 1
John Canny 1
Deepak Ajwani 1
Patrick Nicholson 1
Fengyuan Zhu 1
Dominique Laurent 1
Yeowwei Choong 1
Luca Becchetti 1
Ying Cui 1
Meghana Deodhar 1
Keli Xiao 1
Patrick Haffner 1
Zhili Zhang 1
Qingyan Yang 1
Scott Burton 1
Christos Boutsidis 1
Hui Ke 1
Tamara Kolda 1
Yulan He 1
Bingsheng Wang 1
Jie Wang 1
Karthik Subbian 1
João Duarte 1
Galileo Namata 1
John Frenzel MD 1
Hua Duan 1
Yandong Liu 1
Joshua Vogelstein 1
Qiaozhu Mei 1
Takeshi Yamada 1
Suresh Iyengar 1
Jiawei Han 1
Ashwin Machanavajjhala 1
Edward Clarkson 1
Fuxin Li 1
Erheng Zhong 1
Wei Fan 1
Eder Carvalho 1
Ali Hemmatyar 1
Meng Jiang 1
Yi Zhen 1
Beilun Wang 1
Chihya Shen 1
Zhitao Wang 1
Jingrui He 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
Ali Khodadadi 1
Bo Long 1
Hans Kriegel 1
Gunjan Gupta 1
Diana Inkpen 1
Ling Feng 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
Kuan Zhang 1
Vetle Torvik 1
Luigi Moccia 1
Edoardo Serra 1
Nesreen Ahmed 1
Claudio Schifanella 1
Min Wang 1
Shuiwang Ji 1
Ali Pınar 1
Ling Chen 1
Michail Vlachos 1
Yang Liu 1
Chunxiao Xing 1
Dechuan Zhan 1
Ruggero Pensa 1
Jose Hern´ndez-Orallo 1
Rainer Gemulla 1
Saurabh Paul 1
Guangtao Wang 1
Xueying Zhang 1
Yiping Ke 1
Evrim Acar 1
Yang Zhou 1
Ben London 1
Joseph Ruiz Md 1
Masahiro Kimura 1
Alexander Ihler 1
Kaiwei Chang 1
Forrest Briggs 1
Gustavo Batista 1
Qiang Zhu 1
Philip Yu 1
Jure Leskovec 1
Maya Bercovitch 1
Minsoo Jung 1
Jessica Lin 1
Xing Wang 1
Ramakrishnan Kannan 1
Frédéric Rayar 1
Sabine Barrat 1
Peng Mi 1
Arnab Nandi 1
Axel Soto 1
Boyue Wang 1
Shebuti Rayana 1
Michalis Faloutsos 1
Hongxia Yang 1
Haoda Fu 1
Dawei Zhou 1
Jingrui He 1
Liming Chen 1
Tetsuji Ogawa 1
Yuto Yamaguchi 1
Shaoxu Song 1
Guangneng HU 1
Gianni Costa 1
Stefan Kramer 1
Luke McDowell 1
Huaimin Wang 1
Qiang You 1
Miao Tian 1
Qi Tian 1
Jennifer Neary 1
Minoru Kanehisa 1
Irwin King 1
Ling Liu 1
Huilei He 1
Fei Zou 1
Hua Wang 1
Virgílio Almeida 1
Christos Faloutsos 1
Laiwan Chan 1
Nitin Agarwal 1
S Muthukrishnan 1
Kunta Chuang 1
Anthony Tung 1
Teresa Tjahja 1
Yuanli Pei 1
Wenchih Peng 1
Zekai Gao 1
Sutharshan Rajasegarar 1
Jeffrey Chan 1
Laura Smith 1
Jin Zhang 1
Amin Saberi 1
Adelelu Jia 1
Alexandru Iosup 1
Aniket Chakrabarti 1
Reza Zafarani 1
Saurabh Kataria 1
Matthew Rattigan 1
Geoffrey Barbier 1
Limin Yao 1
Cheukkwong Lee 1
Olvi Mangasarian 1
Chris Clifton 1
Mohammed Zaki 1
Jennifer Dy 1
Shaojun Wang 1
Loïc Cerf 1
Henry Tan 1
Shanshan Feng 1
Guannan Liu 1
Liang Wang 1
Kimon Fountoulakis 1
Gianluigi Greco 1
Francesco Gullo 1
Guimei Liu 1
Min Ding 1
Jennifer Neville 1
Gensheng Zhang 1
Yiming Yang 1
Vassilios Vassiliadis 1
Kaiming Ting 1
Christophe Giraud-Carrier 1
Ayan Acharya 1
Sreangsu Acharyya 1
Zhiqiang Xu 1
Zhongfei Zhang 1
Matthew Rowe 1
Changtien Lu 1
Aditya Menon 1
Edward Chang 1
Kazumi Saito 1
Chengxiang Zhai 1
Dong Xin 1
Christian Böhm 1
Gianlorenzo D'Angelo 1
Yu Zheng 1
Saurav Sahay 1
Xiaowen Ding 1
Jörg Sander 1
Siyuan Liu 1
Maria Halkidi 1
Tanmoy Chakraborty 1
David Leake 1
Chenguang Wang 1
Zhoujun Li 1
Neilzhenqiang Gong 1
Yi Chang 1
Qiang Wei 1
David Gleich 1
Steven Hoi 1
Luming Zhang 1
Lei Zou 1
Jian Wang 1
Manos Papagelis 1
Ruud Van De Bovenkamp 1
Clyde Giles 1
Wei Peng 1
David Jensen 1
Tengfei Bao 1
Brook Wu 1
Glenn Fung 1
Guangyong Chen 1
Richard Ma 1
Fabrizio Marozzo 1
Domenico Talia 1
Zeeshan Syed 1
Kamalakar Karlapalem 1
Dimitrios Mavroeidis 1
Jean Boulicaut 1
Dale Schuurmans 1
Peer Kröger 1
Céline Robardet 1
Zengjian Hu 1
Boaz Ben-Moshe 1
Pradeep Tamma 1
Bill Howe 1
Nicholasjing Yuan 1
Yu Yang 1
Ahmet Sarıyüce 1
Changtien Lu 1
Sen Wang 1
Dafna Shahaf 1
Stephen Fienberg 1
Raviv Raich 1
Bilson Campana 1
Vibhor Rastogi 1
Deng Cai 1
Sarit Kraus 1
Sigal Sina 1
Chris Ding 1
Lior Rokach 1
Dityan Yeung 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
Raheleh Makki 1
Maria De Oliveira 1
Tina Eliassi-Rad 1
Yanjun Qi 1
Theodoros Lappas 1
Munmun De Choudhury 1
Wenjie Li 1
Yada Zhu 1
Lorenzo De Stefani 1
Alessandro Epasto 1
Caetanotraina Jr 1
Oualid Boutemine 1
Yixuan Li 1
Victorjunqiu Wei 1
Lorenzo Severini 1
Xing Yong 1
Ou Wu 1
Lei Ma 1
T Murali 1
Kiyoko Aoki-Kinoshita 1
Sudhir Kumar 1
Ravi Janardan 1
Tiancheng Lou 1
Giacomo Berardi 1
Xiaotong Zhang 1
Han Liu 1
Xiaodan Song 1

Affiliation Paper Counts
University of Notre Dame 1
Nanjing University of Aeronautics and Astronautics 1
University of Florence 1
University of Connecticut 1
Hong Kong Red Cross Blood Transfusion Service 1
Nokia USA 1
Waseda University 1
Universite Claude Bernard Lyon 1 1
Lancaster University 1
Oak Ridge National Laboratory 1
Osaka University 1
University of Iowa 1
National University of Defense Technology China 1
Wright-Patterson AFB 1
Eli Lilly and Company 1
Swiss Federal Institute of Technology, Zurich 1
Lawrence Livermore National Laboratory 1
University of Rochester 1
Naval Research Laboratory 1
Stevens Institute of Technology 1
Jerusalem College of Technology 1
National Taiwan University of Science and Technology 1
Oracle Corporation 1
Lanzhou University 1
University of New South Wales 1
Northeastern University 1
Research Organization of Information and Systems National Institute of Informatics 1
University of Malaya 1
Queen's University Belfast 1
University of Milan 1
Temple University 1
Syracuse University 1
Umea University 1
Curtin University of Technology, Perth 1
University of Gottingen 1
University at Buffalo, State University of New York 1
US Naval Academy 1
University of Roma La Sapienza 1
Griffith University 1
University of New Mexico 1
Alexandria University 1
Saarland University 1
University of Kuwait 1
Vilnius University 1, Inc. 1
Harvard School of Engineering and Applied Sciences 1
Ariel University Center of Samaria 1
Siemens USA 1
eBay, Inc. 1
Yuncheng University 1
Innopolis University 1
IBM, India 1
University of Montpellier 1
Twitter, Inc. 1
Ryukoku University 1
Universite Lille 2 Droit et Sante 1
California State University Fullerton 1
University of Michigan 1
Anhui University 1
University of Ontario Institute of Technology 1
Universite de Cergy-Pontoise 1
National Technical University of Athens 1
Princeton University 1
Claremont Graduate University 1
Queens College, City University of New York 1
Iowa State University 1
University of Arkansas - Fayetteville 1
North Carolina State University 1
Korea University 1
Yale University 1
University of Auckland 1
Los Alamos National Laboratory 1
University of Missouri-Columbia 1
John F. Kennedy School of Government 1
City University of New York 1
University of South Florida Tampa 1
Valley Laboratory 1
University of Salford 1
Hong Kong Polytechnic University 1
Australian National University 1
Sabanci University 1
University of Massachusetts Dartmouth 1
University of Texas at Dallas 1
University of Vermont 1
University of Arizona 1
Southwestern University 1
Washington University in St. Louis 1
Soochow University 1
HP Labs 1
Universidad Politecnica de Valencia 1
State University of New York at Albany 1
BBN Technologies 1
Air Force Research Laboratory Information Directorate 1
University of Shizuoka 1
National Chiao Tung University Taiwan 1
MITRE Corporation 1
Norwegian University of Science and Technology 1
Indian Institute of Science, Bangalore 1
University of Tsukuba 1
Zhejiang Wanli University 1
Aston University 1
University of Hawaii at Hilo 1
Colorado School of Mines 1
Georgia Tech Research Institute 1
University of Louisiana at Lafayette 1
Sandia National Laboratories, California 1
John Carroll University 1
Radboud University Nijmegen 1
Brigham and Women's Hospital 1
University of Toronto 1
De Montfort University 1
INRIA Lorraine 1
Florida Atlantic University 1
Wright State University 1
Air Force Research Laboratory 1
Macquarie University 1
University of West Florida 1
Shenyang Institute of Automation Chinese Academy of Sciences 1
Thapar University 2
University of Glasgow 2
Hefei University of Technology 2
Zhejiang University 2
Institute of High Performance Computing, Singapore 2
Johns Hopkins University 2
University of Electronic Science and Technology of China 2
Tel Aviv University 2
University of Minnesota System 2
University of Houston 2
The University of Hong Kong 2
Brigham Young University 2
The University of North Carolina at Charlotte 2
Harvard University 2
Nanjing University of Science and Technology 2
Istituto Di Calcolo E Reti Ad Alte Prestazioni, Rende 2
Polytechnic Institute of Turin 2
Montclair State University 2
South National University 2
Hong Kong Baptist University 2
University of California, Davis 2
Drexel University 2
Bilkent University 2
University of Kansas Lawrence 2
Singapore Management University 2
University of Nebraska at Omaha 2
University of Quebec in Outaouais 2
Institute for Systems and Computer Engineering of Porto 2
Indiana University 2
University of Virginia 2
Industrial Technology Research Institute of Taiwan 2
Missouri University of Science and Technology 2
University of Maryland, Baltimore County 2
University of California, Berkeley 2
University of Tokyo 2
Huazhong University of Science and Technology 2
Nokia Corporation 2
University of California, Los Angeles 2
University of Quebec in Montreal 2
University of Ottawa, Canada 2
University of Athens 2
IBM Zurich Research Laboratory 2
Kent State University 2
University of California, San Diego 2
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 2
Microsoft Research Asia 2
Vishwakarma Institute of Technology 2
Qatar Computing Research institute 2
Facebook, Inc. 2
International Institute of Information Technology Hyderabad 3
Max Planck Institute for Informatics 3
Shandong University of Science and Technology 3
Bar-Ilan University 3
University of Hildesheim 3
Indian Institute of Technology, Kharagpur 3
University of Pennsylvania 3
University of California, Irvine 3
The University of British Columbia 3
Seoul National University 3
University of Texas M. D. Anderson Cancer Center 3
Imperial College London 3
University of Kentucky 3
INSA Lyon 3
Academia Sinica Taiwan 3
Institute of Automation Chinese Academy of Sciences 3
Xerox Corporation 3
Binghamton University State University of New York 3
Italian National Research Council 3
Nokia Bell Labs 3
Beijing University of Technology 3
University of Massachusetts Boston 3
Wuhan University 3
Southern Illinois University at Carbondale 3
University of Alberta 3
University of Queensland 3
Johannes Gutenberg University Mainz 3
Universite Francois-Rabelais Tours 3
The Chinese University of Hong Kong, Shenzhen 3
Emory University 4
Institute for Infocomm Research, A-Star, Singapore 4
Brookhaven National Laboratory 4
Universitat Politecnica de Catalunya 4
The University of Western Ontario 4
IBM Research 4
Brown University 4
University of Antwerp 4
Beihang University 4
AT&T Inc. 4
University of Washington, Seattle 4
Dalhousie University 4
Athens University of Economics and Business 4
Monash University 4
Boston University 4
Shanghai Jiaotong University 4
University of Sydney 4
University Michigan Ann Arbor 4
Microsoft Corporation 4
University of Pisa 4
Yahoo Research Barcelona 4
Case Western Reserve University 5
University of Texas at San Antonio 5
Rice University 5
Sandia National Laboratories, New Mexico 5
University of Southern California, Information Sciences Institute 5
George Mason University 5
Chinese Academy of Sciences 5
New Jersey Institute of Technology 5
The University of North Carolina at Chapel Hill 5
University of Southern California 5
Rutgers, The State University of New Jersey 5
Dalian University of Technology 6
Delft University of Technology 6
AT&T Laboratories Florham Park 6
Kyoto University 6
University of Turin 6
University of Massachusetts Amherst 6
National University of Singapore 6
Nippon Telegraph and Telephone Corporation 6
Ludwig Maximilian University of Munich 6
University of California, Santa Barbara 6
University of Minnesota Twin Cities 6
Yahoo Inc. 6
Google Inc. 7
Purdue University 7
University of Florida 7
Renmin University of China 7
University of Maryland 7
Massachusetts Institute of Technology 7
Ben-Gurion University of the Negev 7
Sharif University of Technology 7
University of California, Riverside 7
Federal University of Minas Gerais 7
University of Wisconsin Madison 7
Rutgers University-Newark Campus 8
Pennsylvania State University 8
Nanyang Technological University 8
University of Texas at Austin 8
Xi'an Jiaotong University 8
Aalto University 8
Ohio State University 9
University of Sao Paulo 9
Oregon State University 9
Microsoft Research 9
Yahoo Research Labs 9
National Taiwan University 10
Stanford University 10
Peking University 10
Florida International University 10
IBM Thomas J. Watson Research Center 10
Stony Brook University 10
University of Melbourne 10
University of Illinois at Chicago 10
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
Virginia Tech 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
University of Illinois at Urbana-Champaign 19
NEC Laboratories America, Inc. 19
Chinese University of Hong Kong 22
Tsinghua University 37
Carnegie Mellon University 44
Arizona State University 49

ACM Transactions on Knowledge Discovery from Data (TKDD) - Survey Papers and Regular Papers

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

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

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

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)

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

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

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

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

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

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

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

Volume 1 Issue 3, December 2007
Volume 1 Issue 2, August 2007
Volume 1 Issue 1, March 2007
All ACM Journals | See Full Journal Index

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