ACM Transactions on

Knowledge Discovery from Data (TKDD)

Latest Articles

Social Network Monitoring for Bursty Cascade Detection

Exploring Multiobjective Optimization for Multiview Clustering

Generating Realistic Synthetic Population Datasets

Motif Counting Beyond Five Nodes


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
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.

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.

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.

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.

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.

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.

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.

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.

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 347
Citation Count 3017
Available for Download 347
Downloads (6 weeks) 3659
Downloads (12 Months) 30646
Downloads (cumulative) 223248
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)
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
John Hopcroft 4
Nikolaj Tatti 4
Philip YU 4
Zhiwen Yu 4
Hanghang Tong 4
Zhihua Zhou 4
Shenghuo Zhu 4
Jilles Vreeken 4
Heng Huang 4
Feiping Nie 4
Bin Guo 4
Huan Liu 4
Hong Cheng 4
Christopher Jermaine 4
Guofei Jiang 3
Lise Getoor 3
Jure Leskovec 3
Malik Magdon-Ismail 3
Mingsyan Chen 3
Qi Liu 3
Enhong Chen 3
Xiaoli Fern 3
Hamid Rabiee 3
Yun Chi 3
Yasushi Sakurai 3
Evimaria Terzi 3
Yihong Gong 3
Lei Tang 3
Srinivasan Parthasarathy 3
Jirong Wen 3
Naren Ramakrishnan 3
Jennifer Neville 3
Dingding Wang 3
Fabio Fassetti 3
Chengqi Zhang 3
Fabrizio Angiulli 3
Maryam Ramezani 2
Yong Ge 2
Xianchao Zhang 2
Dacheng Tao 2
Belle Tseng 2
Wei Ding 2
Vivekanand Gopalkrishnan 2
Jie Tang 2
U Kang 2
Eugene Agichtein 2
Hong Xie 2
Christopher Leckie 2
Carlotta Domeniconi 2
Sanjay Ranka 2
Kui Yu 2
Dantong Yu 2
Jiliang Tang 2
Charalampos Tsourakakis 2
Bryan Hooi 2
Martin Ester 2
Hari Sundaram 2
Mohamed Bouguessa 2
Spiros Papadimitriou 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
Kijung Shin 2
Kristina Lerman 2
Pinghui Wang 2
Hao Huang 2
Hong Qin 2
Yehuda Koren 2
Arnold Boedihardjo 2
Heikki Mannila 2
Panayiotis Tsaparas 2
Chen Chen 2
Leman Akoglu 2
Zhu Wang 2
Jianhui Chen 2
Yu Zhang 2
Ali Khodadadi 2
Jingrui He 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
Jie Tang 2
Andrea Esuli 2
Petros Drineas 2
JiLei Tian 2
Ping Luo 2
B Prakash 2
Yuru Lin 2
Shinjae Yoo 2
Ian Davidson 2
Sanjay Chawla 2
Brian Gallagher 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
Hao Wu 2
Sucheta Soundarajan 2
Matteo Riondato 2
Fatma Bouali 1
Gilles Venturini 1
Maoyuan Sun 1
Rosane Minghim 1
Srayan Datta 1
Eytan Adar 1
Maryam Tahani 1
Ahmed El-Mahdy 1
Jeffreyxu Yu 1
Soroush Vosoughi 1
Vishal Kaushal 1
Yunfei Lu 1
Wenwu Zhu 1
Michele Garetto 1
Junming Shao 1
Yllka Velaj 1
Xiaojun Chang 1
Lars Schmidt-Thieme 1
Dityan Yeung 1
Michael Lyu 1
Fengyuan Zhu 1
Xiaoming Fu 1
Deepak Ajwani 1
Patrick Nicholson 1
Xiuzhen Zhang 1
George Karypis 1
Davoud Moulavi 1
Koji Hino 1
Masaru Kitsuregawa 1
Evangelos Papalexakis 1
Nicholas Sidiropoulos 1
Jilei Tian 1
Xiang Zhang 1
Jenwei Huang 1
James Bailey 1
Jianping Zhang 1
Graham Cormode 1
Manas Somaiya 1
Fei Yi 1
Jevin West 1
Biao Xiang 1
Yi Zheng 1
Ting Guo 1
Jia Wu 1
Xingquan Zhu 1
Xun Tang 1
Jun Yan 1
James BEZDEK 1
Marimuthu Palaniswami 1
Bin Li 1
Jayavardhana Gubbi 1
Fernando Kuipers 1
Dick Epema 1
Min Wang 1
Linpeng Tang 1
Marc Maier 1
Lionel Ni 1
MingXi Wu 1
Benjamin Fung 1
Ye Chen 1
John Canny 1
Dominique Laurent 1
Yeowwei Choong 1
Luca Becchetti 1
Ying Cui 1
Meghana Deodhar 1
Keli Xiao 1
Bo Long 1
Hans Kriegel 1
Gunjan Gupta 1
Ling Feng 1
Diana Inkpen 1
Kuan Zhang 1
Vetle Torvik 1
Wei Xie 1
Jing Xiao 1
Prithwish Chakraborty 1
Alessandro Panconesi 1
Edoardo Serra 1
Luigi Moccia 1
Claudio Schifanella 1
Nesreen Ahmed 1
Min Wang 1
Shuiwang Ji 1
Ali Pınar 1
Michail Vlachos 1
Ling Chen 1
Yang Liu 1
Chunxiao Xing 1
Dechuan Zhan 1
Ruggero Pensa 1
Saurabh Paul 1
Rainer Gemulla 1
Guangtao Wang 1
Xueying Zhang 1
Jose Hern´ndez-Orallo 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
Boyue Wang 1
Michalis Faloutsos 1
Hongxia Yang 1
Haoda Fu 1
Dawei Zhou 1
Jingrui He 1
Liming Chen 1
Frédéric Rayar 1
Sabine Barrat 1
Peng Mi 1
Axel Soto 1
Ramakrishnan Kannan 1
Arnab Nandi 1
Shebuti Rayana 1
Tetsuji Ogawa 1
Yuto Yamaguchi 1
Guangneng HU 1
Shaoxu Song 1
Gianni Costa 1
Stefan Kramer 1
Huaimin Wang 1
Qiang You 1
Luke McDowell 1
Miao Tian 1
Qi Tian 1
Jennifer Neary 1
Minoru Kanehisa 1
Irwin King 1
Seyyed Hosseini 1
Erfan Tavakoli 1
Jordan Bakerman 1
Gianlorenzo D'Angelo 1
Yu Zheng 1
Saurav Sahay 1
Geoffrey Fairchild 1
Qi Tan 1
Ling Liu 1
Hua Wang 1
Huilei He 1
Fei Zou 1
Laiwan Chan 1
Virgílio Almeida 1
Christos Faloutsos 1
Nitin Agarwal 1
S Muthukrishnan 1
Kunta Chuang 1
Anthony Tung 1
Shanshan Feng 1
Guannan Liu 1
Liang Wang 1
Kimon Fountoulakis 1
Teresa Tjahja 1
Zekai Gao 1
Yuanli Pei 1
Wenchih Peng 1
Sutharshan Rajasegarar 1
Jeffrey Chan 1
Laura Smith 1
Jin Zhang 1
Amin Saberi 1
Alexandru Iosup 1
Aniket Chakrabarti 1
Reza Zafarani 1
Adelelu Jia 1
Saurabh Kataria 1
Matthew Rattigan 1
Limin Yao 1
Geoffrey Barbier 1
Cheukkwong Lee 1
Olvi Mangasarian 1
Chris Clifton 1
Mohammed Zaki 1
Jennifer Dy 1
Shaojun Wang 1
Henry Tan 1
Loïc Cerf 1
Stefano Leucci 1
Gianluigi Greco 1
Francesco Gullo 1
Guimei Liu 1
Min Ding 1
Xiaowen Ding 1
Guangyong Chen 1
Sharad Mehrotra 1
Jiahui Zhu 1
Richard Ma 1
Fabrizio Marozzo 1
Domenico Talia 1
Jörg Sander 1
Siyuan Liu 1
Maria Halkidi 1
Ahmet Sarıyüce 1
Changtien Lu 1
Bill Howe 1
Nicholasjing Yuan 1
Yu Yang 1
Sen Wang 1
Chong Peng 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
Jian Wang 1
Lei Zou 1
Luming Zhang 1
Ruud Van De Bovenkamp 1
Clyde Giles 1
Manos Papagelis 1
Wei Peng 1
David Jensen 1
Tengfei Bao 1
Brook Wu 1
Glenn Fung 1
Zeeshan Syed 1
Kamalakar Karlapalem 1
Dale Schuurmans 1
Dimitrios Mavroeidis 1
Peer Kröger 1
Jean Boulicaut 1
Pradeep Tamma 1
Zengjian Hu 1
Boaz Ben-Moshe 1
Neil Smalheiser 1
Gensheng Zhang 1
Yiming Yang 1
Vassilios Vassiliadis 1
Kaiming Ting 1
Christophe Giraud-Carrier 1
Ayan Acharya 1
Sreangsu Acharyya 1
Changtien Lu 1
Zhiqiang Xu 1
Aditya Menon 1
Zhongfei Zhang 1
Matthew Rowe 1
Edward Chang 1
Kazumi Saito 1
Chengxiang Zhai 1
Dong Xin 1
Dafna Shahaf 1
Stephen Fienberg 1
Raviv Raich 1
Bilson Campana 1
Christian Böhm 1
Vibhor Rastogi 1
Deng Cai 1
Sigal Sina 1
Sarit Kraus 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
Tina Eliassi-Rad 1
Yanjun Qi 1
Theodoros Lappas 1
Munmun De Choudhury 1
Wenjie Li 1
Raheleh Makki 1
Maria De Oliveira 1
Zhicheng Liu 1
Changhyun Lee 1
Niranjan Kamat 1
Yada Zhu 1
Caetanotraina Jr 1
Yixuan Li 1
Victorjunqiu Wei 1
James Cheng 1
Shachar Kaufman 1
Ori Stitelman 1
Leland Wilkinson 1
Céline Robardet 1
Hongfu Liu 1
Yun Fu 1
Tania Ghosh 1
José Balcázar 1
Weekeong Ng 1
Mengling Feng 1
Xiao Jiang 1
Lyle Ungar 1
Hockhee Ang 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
Jinpeng Wang 1
Josep Larriba-Pey 1
Arnau Prat-Pérez 1
Risa Myers 1
Qingtian Zeng 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
Victor Lee 1
Robert Kleinberg 1
Zhi Yang 1
Yafei Dai 1
Edgar Treviño 1
Moshe Kam 1
Jieping Ye 1
Licong Cui 1
Xiaofeng Zhu 1
Lorenzo De Stefani 1
Alessandro Epasto 1
Oualid Boutemine 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
Tiancheng Lou 1
Guna Seetharaman 1
Ming Shao 1
Yun Fu 1
Karl Pazdernik 1
Hong Huang 1
Loris Belcastro 1
Giacomo Berardi 1
Xiaotong Zhang 1
Han Liu 1
Kathleen Carley 1
Xiaodan Song 1
Yasuhiro Fujiwara 1
Sebastian Moreno 1
Sergey Kirshner 1
Wei Wang 1
ChienWei Chen 1
Weiyin Loh 1
John Salerno 1
Nitin Kumar 1
Flip Korn 1
Feng Chen 1
Eugenia Kontopoulou 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
Lei Li 1
Ke Wang 1
Xinran He 1
Jing Zhang 1
Chris Ding 1
Paul Thompson 1
Haesun Park 1
Charles Stolper 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
Sheng Li 1
Rian Bahran 1
Yuxiao Dong 1
Alessandra Sala 1
Min Peng 1
Christos Faloutsos 1
Jerry Kiernan 1
Sitaram Asur 1
Kevin Yip 1
Wei Zheng 1
Zhenxing Wang 1
Ümit Çatalyürek 1
Xutong Liu 1
Yencheng Lu 1
Xue Li 1
Guodong Long 1
Jie Cheng 1
Thomas Reichherzer 1
Carlos Lorenzetti 1
Dan Roth 1
Ephraim Korach 1
Jeffrey Ullman 1
Kai Zheng 1
Wenyuan Zhu 1
Zhongyuan Wang 1
Allon Percus 1
Xunhua Guo 1
Ravi Konuru 1
Baoxing Huai 1
Hengshu Zhu 1
Benoît Dumoulin 1
Xiuyao Song 1
John Gums 1
Yin Zhang 1
Zhongfei Zhang 1
Yunxin Zhao 1
Jude Shavlik 1
Edward Toth 1
Jianzong Wang 1
Qian Sun 1
Marco Bressan 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
Patrick Haffner 1
Zhili Zhang 1
Qingyan Yang 1
Christos Boutsidis 1
Scott Burton 1
Bingsheng Wang 1
Hui Ke 1
Tamara Kolda 1
Jie Wang 1
Galileo Namata 1
João Duarte 1
Yulan He 1
Karthik Subbian 1
John Frenzel MD 1
Hua Duan 1
Yandong Liu 1
Qiaozhu Mei 1
Joshua Vogelstein 1
Takeshi Yamada 1
Suresh Iyengar 1
Jiawei Han 1
Ashwin Machanavajjhala 1
Erheng Zhong 1
Wei Fan 1
Yi Zhen 1
Nick Street 1
Pritam Gundecha 1
Fan Guo 1
Edward Wild 1
Murat Kantarcıoğlu 1
John Guttag 1
Marc Plantevit 1
Jinlin Chen 1
Shantanu Godbole 1
Alin Dobra 1
Binay Bhattacharya 1
Anushka Anand 1
Bin Zhou 1
Indrajit Banerjee 1
Yicheng Tu 1
Dan Simovici 1
Hao Wang 1
Siddharth Gopal 1
Madhav Jha 1
Renato Assunção 1
Alice Leung 1
Subhabrata Sen 1
Dino Ienco 1
Rosa Meo 1
Hongliang Fei 1
Jun Huan 1
Carlos Garcia-Alvarado 1
Eduardo Hruschka 1
Ana Appel 1
Jeffreyxu Yu 1
Zhen Guo 1
Yashu Liu 1
Faming Lu 1
Andrew Mehler 1
Stephen North 1
Seungil Huh 1
Chojui Hsieh 1
Chihjen Lin 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
Tim Oates 1
Beilun Wang 1
Chihya Shen 1
Zhitao Wang 1
Eder Carvalho 1
Edward Clarkson 1
Fuxin Li 1
Meng Jiang 1
Lei Xie 1
Ali Hemmatyar 1
Hyunah Song 1
Haifeng Chen 1
Xiang Zhang 1
Nenghai Yu 1
Manasi Patwardhan 1
Divya Pandove 1
Hao Ye 1
Peter Triantafillou 1
Zoran Obradović 1
Wangchien Lee 1
Changshui Zhang 1
Sri Ravana 1
Chris North 1
Hanseung Lee 1
Shiqiang Yang 1
Lei Ying 1
Yu Shi 1
Agma Traina 1
Mostafa Mohsenvand 1
Shivani Goel 1
Kyle Kloster 1
Feifan Fan 1
Tianyang Zhang 1
Shiqiang Yang 1
Emili Leonardi 1
Hamed Bonab 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
Zhanpeng Fang 1
Jing Peng 1
Phengann Heng 1
Alyson Wilson 1
Hua Wang 1
Gang Tian 1
Paolo Trunfio 1
Yang Zhou 1
Dengyong Zhou 1
Xinjiang Lu 1
Ming Zhang 1
Biru Dai 1
Divesh Srivastava 1
Hungleng Chen 1
Zhenjie Zhang 1
Jian Cao 1
Jie Wang 1
Shiyou Qian 1
Kamer Kaya 1
Daniel Halperin 1
Quan Sheng 1
Qiang Cheng 1
Maha Alabduljalil 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
Yao Zhang 1
Aisling Kelliher 1
Paul Castro 1
Rose Yu 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
Manolis Kellis 1
Carlos Castillo 1
Tianbing Xu 1
Sanmay Das 1
Amit Dhurandhar 1
Beechung Chen 1
Fedja Hadzic 1
Elizabeth Chang 1
Aminul Islam 1
Li Wan 1
Weekeong Ng 1
Sethuraman Panchanathan 1
Michael Mampaey 1
Hunghsuan Chen 1
Feida Zhu 1
Flavio Chierichetti 1
Xiaowei Chen 1
Nashreen Nesa 1
Yu Lei 1
Haojun Zhang 1
Limsoon Wong 1
Maria Sapino 1
Shipeng Yu 1
Zhiting Hu 1
Pedro Melo 1
Yuan Jiang 1
Qinbao Song 1
Michele Coscia 1
Yi Wang 1
Charles Elkan 1
João Gama 1
Jaideep Srivastava 1
Carlos Guestrin 1
Tomoharu Iwata 1
Naonori Ueda 1
Qi Lou 1
Wei Fan 1
Xifeng Yan 1
Julian McAuley 1
Pavel Senin 1
Sunil Gandhi 1
Madelaine Daianu 1
Junbin Gao 1
Yanfeng Sun 1
Baocai Yin 1
Feiyu Xiong 1
Fei Wang 1
Shiqiang Tao 1
Guoqiang Zhang 1
Esther Galbrun 1
Hannah Kim 1
John Stasko 1
Jingchao Ni 1
Alceu Costa 1
Rinkl Rani 1
David Bindel 1
Cheng Long 1
Shujian Huang 1
Yihan Wang 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
Aparna Varde 1
Haiqin Yang 1
Hongxia Yang 1
Jiongqian Liang 1
Xuhui Li 1
Yanchun Zhang 1
Ricardo Campello 1
Qiang Qu 1
Shuhui Wang 1
Jeffrey Chan 1
Michael Houle 1
Pedro Vaz De Melo 1
Dimitrios Gunopulos 1
Daxin Jiang 1
Erik Saule 1
Can Chen 1
Tao Ku 1
Seunghee Bae 1
Yunhong Hu 1
Abhisek Kundu 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
Collin Stultz 1
Maguelonne Teisseire 1
Boleslaw Szymanski 1
Paolo Boldi 1
Lini Thomas 1
Sachindra Joshi 1
Tharam Dillon 1
Yixin Chen 1
Xuanhong Dang 1
Shumo Chu 1
Timothy La Fond 1
Ravi Kumar 1
Yue Ning 1
Luigi Pontieri 1
Bingrong Lin 1
Francesco Bonchi 1
Kasim Candan 1
Sunil Vadera 1
S Upham 1
Hongzhi Yin 1
Thomas Porta 1
Jeffrey Erman 1
Ming Li 1
Dora Erdős 1
Kaiyuan Zhang 1
Carlos Ordonez 1
Fosca Giannotti 1
James Cheng 1
Joydeep Ghosh 1
Peter Christen 1
Daniel Dunlavy 1
Christos Doulkeridis 1
David Dominguez-Sal 1
Steven Skiena 1
Hiroshi Motoda 1
Chris Volinsky 1
Danai Koutra 1
Andreas Krause 1
Hsiangfu Yu 1
Aditya Parameswaran 1
Binbin Lin 1
Johannes Gehrke 1
Christo Wilson 1
Ben Zhao 1
Crystal Chen 1
Susan Frankenstein 1
Feng Tian 1
Juanzi Li 1
Javier Barria 1
Silei Xu 1
Bo Liu 1
Leonid Hrebien 1
Pei Yang 1
Li Li 1
Denian Yang 1
Zhishan Guo 1
Yunsing Koh 1
Stephen Brooks 1
Evangelos Milios 1
Jaegul Choo 1
Ashton Anderson 1
Sendhil Mullainathan 1
Qing He 1
Kai Zhang 1
Kun He 1
Fengyu Qiu 1
Yue Wu 1
Edward Chang 1
Christos Anagnostopoulos 1
Carla Chiasserini 1
Fazli Can 1
Bryan Perozzi 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
Wei Fan 1
Nitesh Chawla 1
Yasser Altowim 1
Dmitri Kalashnikov 1
Pei Yang 1
Rezwan Ahmed 1
Wei Wei 1
Duygu Ucar 1
Mustafa Bilgic 1
Ben Kao 1
David Cheung 1
Muna Al-Razgan 1
Guoqing Chen 1
Martin Rosvall 1
Xiaojun Chang* 1
Lina Yao 1
Zhao Kang 1
Xin Jin 1
Tao Yang 1
Polina Rozenshtein 1
Ana Maguitman 1
Filippo Menczer 1
Rómer Rosales 1
Xiaofang Zhou 1
Xindong Wu 1
Foto Afrati 1
Fangtao Li 1
Junjie Wu 1
Cheng Zeng 1
Atreya Srivathsan 1
Tong Sun 1
Yanchi Liu 1
Songhua Xu 1
Duo Zhang 1
Kun Liu 1
Dmitry Pavlov 1
Raymond Ng 1
Piotr Indyk 1
Anne Laurent 1
Christopher Carothers 1
Satyanarayana Valluri 1
Ashish Verma 1
Raghu Ramakrishnan 1
Rong Ge 1
Byronju Gao 1
Li Tu 1
Saharon Rosset 1
Claudia Perlich 1
Tuannhon Dang 1
Jérémy Besson 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
Lei Shi 1
Yang Yang 1
Muhammad Hameed 1
Yongli Hu 1
Ying Wei 1
Yubao Wu 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
South China University of Technology 1
National Central University Taiwan 1
Wright-Patterson AFB 1
Eli Lilly and Company 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
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
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
Universidad Adolfo Ibanez 1
Iowa State University 1
University of Arkansas - Fayetteville 1
Korea University 1
Yale University 1
University of Auckland 1
South China Normal University 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 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
RMIT University 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
Victoria University Melbourne 2
The University of Hong Kong 2
Brigham Young University 2
The University of North Carolina at Charlotte 2
University of Massachusetts Dartmouth 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
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
Swiss Federal Institute of Technology, Zurich 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
International Institute of Information Technology Hyderabad 3
Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India 3
Shandong University of Science and Technology 3
Bar-Ilan University 3
University of Hildesheim 3
Indian Institute of Technology, Kharagpur 3
Los Alamos National Laboratory 3
University of Pennsylvania 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
Lawrence Livermore National Laboratory 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
Qatar Computing Research institute 3
The Chinese University of Hong Kong, Shenzhen 3
Facebook, Inc. 3
Max Planck Institute for Informatics 4
Emory University 4
Institute for Infocomm Research, A-Star, Singapore 4
North Carolina State University 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
Singapore Management University 4
Athens University of Economics and Business 4
Monash University 4
Boston University 4
Shanghai Jiaotong University 4
University Michigan Ann Arbor 4
Microsoft Corporation 4
Northeastern University 4
University of Pisa 4
University of Roma La Sapienza 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 Sydney 5
University of Southern California 5
Rutgers, The State University of New Jersey 5
Dalian University of Technology 6
Delft University of Technology 6
University of California, Irvine 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
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
Wuhan University 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
Google Inc. 8
University of Texas at Austin 8
Xi'an Jiaotong University 8
Ohio State University 9
University of Sao Paulo 9
Purdue University 9
Oregon State University 9
Microsoft Research 9
Yahoo Research Labs 9
Aalto University 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
Sharif University of Technology 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
University of Illinois at Urbana-Champaign 19
NEC Laboratories America, Inc. 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)

Volume 12 Issue 4, May 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

Volume 11 Issue 4, August 2017 Special Issue on KDD 2016 and Regular Papers
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Volume 11 Issue 2, December 2016
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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
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Volume 9 Issue 4, June 2015
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Volume 9 Issue 2, November 2014
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Volume 8 Issue 4, October 2014
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Volume 7 Issue 4, November 2013
Volume 7 Issue 3, September 2013 Special Issue on ACM SIGKDD 2012
Volume 7 Issue 2, July 2013
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Volume 6 Issue 4, December 2012 Special Issue on the Best of SIGKDD 2011
Volume 6 Issue 3, October 2012
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Volume 5 Issue 4, February 2012

Volume 5 Issue 3, August 2011
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Volume 5 Issue 1, December 2010
Volume 4 Issue 3, October 2010
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Volume 3 Issue 4, November 2009
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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
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