ACM Transactions on

Knowledge Discovery from Data (TKDD)

Latest Articles

Social Network Monitoring for Bursty Cascade Detection

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.

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.

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.

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.

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.

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 345
Citation Count 3015
Available for Download 345
Downloads (6 weeks) 3607
Downloads (12 Months) 30187
Downloads (cumulative) 325301
Average downloads per article 943
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
Jieping Ye 7
Hui Xiong 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
Enhong Chen 3
Qi Liu 3
Xiaoli Fern 3
Jilles Vreeken 3
Yun Chi 3
Evimaria Terzi 3
Yasushi Sakurai 3
Yihong Gong 3
Lei Tang 3
Hamid Rabiee 3
Srinivasan Parthasarathy 3
Jirong Wen 3
Dingding Wang 3
Fabio Fassetti 3
Chengqi Zhang 3
Fabrizio Angiulli 3
Wei Ding 2
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
Martin Ester 2
Hari Sundaram 2
Bryan Hooi 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
Naren Ramakrishnan 2
Kristina Lerman 2
Don Towsley 2
Pinghui Wang 2
Hao Huang 2
Hong Qin 2
Kijung Shin 2
Jennifer Neville 2
Yehuda Koren 2
Arnold Boedihardjo 2
Heikki Mannila 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
Ali Khodadadi 2
Jingrui He 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
Petros Drineas 2
JiLei Tian 2
Ping Luo 2
B Prakash 2
Yuru Lin 2
Shinjae Yoo 2
Jie Tang 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
Panayiotis Tsaparas 2
Jimeng Sun 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
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
Yongli Hu 1
Srayan Datta 1
Eytan Adar 1
Muhammad Hameed 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
Dityan Yeung 1
Michael Lyu 1
Jilei Tian 1
Davoud Moulavi 1
Koji Hino 1
Evangelos Papalexakis 1
Nicholas Sidiropoulos 1
George Karypis 1
Masaru Kitsuregawa 1
Xiang Zhang 1
Jenwei Huang 1
James Bailey 1
Jianping Zhang 1
Graham Cormode 1
Manas Somaiya 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
Jun Yan 1
Marimuthu Palaniswami 1
James BEZDEK 1
Jayavardhana Gubbi 1
Bin Li 1
Fernando Kuipers 1
Dick Epema 1
Linpeng Tang 1
Min Wang 1
Marc Maier 1
Lionel Ni 1
Xiaoming Fu 1
Fengyuan Zhu 1
Deepak Ajwani 1
Patrick Nicholson 1
MingXi Wu 1
Benjamin Fung 1
Ye Chen 1
John Canny 1
Dominique Laurent 1
Yeowwei Choong 1
Meghana Deodhar 1
Luca Becchetti 1
Ying Cui 1
Keli Xiao 1
Bo Long 1
Ling Feng 1
Hans Kriegel 1
Gunjan Gupta 1
Diana Inkpen 1
Vetle Torvik 1
Luigi Moccia 1
Edoardo Serra 1
Min Wang 1
Claudio Schifanella 1
Nesreen Ahmed 1
Shuiwang Ji 1
Ali Pınar 1
Ling Chen 1
Michail Vlachos 1
Yang Liu 1
Chunxiao Xing 1
Dechuan Zhan 1
Jose Hern´ndez-Orallo 1
Rainer Gemulla 1
Xueying Zhang 1
Saurabh Paul 1
Guangtao Wang 1
Ruggero Pensa 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
Frédéric Rayar 1
Sabine Barrat 1
Peng Mi 1
Ramakrishnan Kannan 1
Arnab Nandi 1
Axel Soto 1
Boyue Wang 1
Shebuti Rayana 1
Michalis Faloutsos 1
Haoda Fu 1
Kuan Zhang 1
Hongxia Yang 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
Huaimin Wang 1
Miao Tian 1
Qiang You 1
Luke McDowell 1
Qi Tian 1
Jennifer Neary 1
Minoru Kanehisa 1
Irwin King 1
Ling Liu 1
Huilei He 1
Hua Wang 1
Fei Zou 1
Laiwan Chan 1
Virgílio Almeida 1
Christos Faloutsos 1
Nitin Agarwal 1
S Muthukrishnan 1
Kunta Chuang 1
Shanshan Feng 1
Guannan Liu 1
Anthony Tung 1
Liang Wang 1
Kimon Fountoulakis 1
Yuanli Pei 1
Teresa Tjahja 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
Gianlorenzo D'Angelo 1
Yu Zheng 1
Saurav Sahay 1
Xiaowen Ding 1
Jörg Sander 1
Siyuan Liu 1
Maria Halkidi 1
Bill Howe 1
Ahmet Sarıyüce 1
Changtien Lu 1
Geoffrey Fairchild 1
Seyyed Hosseini 1
Erfan Tavakoli 1
Qi Tan 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
Gianluigi Greco 1
Francesco Gullo 1
Guimei Liu 1
Min Ding 1
Gensheng Zhang 1
Yiming Yang 1
Vassilios Vassiliadis 1
Christophe Giraud-Carrier 1
Ayan Acharya 1
Sreangsu Acharyya 1
Kaiming Ting 1
Changtien Lu 1
Zhiqiang Xu 1
Matthew Rowe 1
Aditya Menon 1
Zhongfei Zhang 1
Edward Chang 1
Kazumi Saito 1
Chengxiang Zhai 1
Dong Xin 1
Christian Böhm 1
Dafna Shahaf 1
Stephen Fienberg 1
Raviv Raich 1
Bilson Campana 1
Vibhor Rastogi 1
Deng Cai 1
Chris Ding 1
Sigal Sina 1
Sarit Kraus 1
Lior Rokach 1
Bruno Abrahão 1
Xiaolin Wang 1
Jordan Bakerman 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
Manos Papagelis 1
Ruud Van De Bovenkamp 1
Clyde Giles 1
Wei Peng 1
David Jensen 1
Tengfei Bao 1
Brook Wu 1
Guangyong Chen 1
Sharad Mehrotra 1
Richard Ma 1
Fabrizio Marozzo 1
Domenico Talia 1
Glenn Fung 1
Zeeshan Syed 1
Kamalakar Karlapalem 1
Dimitrios Mavroeidis 1
Dale Schuurmans 1
Peer Kröger 1
Céline Robardet 1
Jean Boulicaut 1
Pradeep Tamma 1
Zengjian Hu 1
Boaz Ben-Moshe 1
Neil Smalheiser 1
James Cheng 1
Shachar Kaufman 1
Ori Stitelman 1
Leland Wilkinson 1
Hockhee Ang 1
José Balcázar 1
Weekeong Ng 1
Tingting Gao 1
Dityan Yeung 1
Longjie Li 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
Lei Ma 1
Ou Wu 1
Xing Yong 1
T Murali 1
Ravi Janardan 1
Kiyoko Aoki-Kinoshita 1
Sudhir Kumar 1
Tiancheng Lou 1
Guna Seetharaman 1
Giacomo Berardi 1
Xiaodan Song 1
Xiaotong Zhang 1
Han Liu 1
Kathleen Carley 1
Yasuhiro Fujiwara 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
Xiao Jiang 1
Lyle Ungar 1
Franco Turini 1
Comandur Seshadhri 1
Luan Tang 1
Quanquan Gu 1
Xintao Wu 1
Jianyong Wang 1
Chun Li 1
Feitony Liu 1
Nick Duffield 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
Zhi Yang 1
Victor Lee 1
Yafei Dai 1
Paul Thompson 1
Haesun Park 1
Charles Stolper 1
Edgar Treviño 1
Moshe Kam 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
Mengling Feng 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
Yun Fu 1
Karl Pazdernik 1
Hong Huang 1
Ming Shao 1
Loris Belcastro 1
Sebastian Moreno 1
Sergey Kirshner 1
Jing Zhang 1
Benoît Dumoulin 1
Xiuyao Song 1
John Gums 1
Yin Zhang 1
Zhongfei Zhang 1
Yunxin Zhao 1
Jude Shavlik 1
Qian Sun 1
Domenico Saccà 1
Sibel Adalı 1
Xiaohui Lu 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
Scott Burton 1
Christos Boutsidis 1
Qingyan Yang 1
Patrick Haffner 1
Zhili Zhang 1
Bingsheng Wang 1
Hui Ke 1
Tamara Kolda 1
Jie Wang 1
Karthik Subbian 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
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
Carlos Lorenzetti 1
Thomas Reichherzer 1
Dan Roth 1
Ephraim Korach 1
Jeffrey Ullman 1
Wenyuan Zhu 1
Kai Zheng 1
Zhongyuan Wang 1
Allon Percus 1
Xunhua Guo 1
Ravi Konuru 1
Baoxing Huai 1
Hengshu Zhu 1
Nick Street 1
Pritam Gundecha 1
Rian Bahran 1
Yuxiao Dong 1
Sheng Li 1
Alessandra Sala 1
Christos Faloutsos 1
Fan Guo 1
Edward Wild 1
Murat Kantarcıoğlu 1
John Guttag 1
Marc Plantevit 1
Jinlin Chen 1
Shantanu Godbole 1
Bin Zhou 1
Alin Dobra 1
Galileo Namata 1
Yulan He 1
João Duarte 1
John Frenzel MD 1
Hua Duan 1
Yandong Liu 1
Joshua Vogelstein 1
Qiaozhu Mei 1
Takeshi Yamada 1
Suresh Iyengar 1
Ashwin Machanavajjhala 1
Jiawei Han 1
Erheng Zhong 1
Wei Fan 1
Edward Clarkson 1
Fuxin Li 1
Eder Carvalho 1
Ali Hemmatyar 1
Meng Jiang 1
Yi Zhen 1
Beilun Wang 1
Chihya Shen 1
Zhitao Wang 1
Lei Xie 1
Hyunah Song 1
Haifeng Chen 1
Xiang Zhang 1
Hao Ye 1
Peter Triantafillou 1
Nenghai Yu 1
Manasi Patwardhan 1
Divya Pandove 1
Anushka Anand 1
Yicheng Tu 1
Dan Simovici 1
Hao Wang 1
Siddharth Gopal 1
Madhav Jha 1
Alice Leung 1
Renato Assunção 1
Eduardo Hruschka 1
Hongliang Fei 1
Jun Huan 1
Dino Ienco 1
Rosa Meo 1
Subhabrata Sen 1
Carlos Garcia-Alvarado 1
Jeffreyxu Yu 1
Ana Appel 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
Jesin Zakaria 1
Thanawin Rakthanmanon 1
Kedar Bellare 1
Brandon Norick 1
Ming Ji 1
Yuval Elovici 1
Ming Lin 1
Changshui Zhang 1
Tim Oates 1
Chris North 1
Hanseung Lee 1
Shiqiang Yang 1
Sri Ravana 1
Wangchien Lee 1
Zoran Obradović 1
Lei Ying 1
Yu Shi 1
Tianyang Zhang 1
Shiqiang Yang 1
Binay Bhattacharya 1
Agma Traina 1
Mostafa Mohsenvand 1
Shivani Goel 1
Kyle Kloster 1
Emili Leonardi 1
Feifan Fan 1
Hamed Bonab 1
Qinli Yang 1
Josif Grabocka 1
Xiang Li 1
Richard Xu 1
Nicolas Schilling 1
David Aha 1
Sougata Mukherjea 1
Ashwin Ram 1
Zhanpeng Fang 1
Jing Peng 1
Yang Zhou 1
Xinjiang Lu 1
Dengyong Zhou 1
Ming Zhang 1
Biru Dai 1
Divesh Srivastava 1
Hungleng Chen 1
Daniel Halperin 1
Jian Cao 1
Jie Wang 1
Shiyou Qian 1
Zhenjie Zhang 1
Kamer Kaya 1
Quan Sheng 1
Qiang Cheng 1
Maha Alabduljalil 1
Niloy Ganguly 1
Sriram Srinivasan 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
Venu Satuluri 1
Hunghsuan Chen 1
Rose Yu 1
Yao Zhang 1
Aisling Kelliher 1
Lian Duan 1
Bruno Ribeiro 1
Siyuan Liu 1
Alyson Wilson 1
Phengann Heng 1
Paolo Trunfio 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
Elizabeth Chang 1
Sanmay Das 1
Amit Dhurandhar 1
Beechung Chen 1
Fedja Hadzic 1
Aminul Islam 1
Li Wan 1
Weekeong Ng 1
Sethuraman Panchanathan 1
Michael Mampaey 1
Yu Lei 1
Haojun Zhang 1
Limsoon Wong 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
Jaideep Srivastava 1
Charles Elkan 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
Sunil Gandhi 1
Paul Castro 1
Madelaine Daianu 1
Hao Wu 1
John Stasko 1
Hannah Kim 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
Tao Mei 1
Quanzeng You 1
Kosuke Hashimoto 1
Nobuhisa Ueda 1
Jie Tang 1
Haiqin Yang 1
Ricardo Campello 1
Aparna Varde 1
Qiang Qu 1
Shuhui Wang 1
Jeffrey Chan 1
Michael Houle 1
Dimitrios Gunopulos 1
Pedro Vaz De Melo 1
Daxin Jiang 1
Seunghee Bae 1
Can Chen 1
Erik Saule 1
Tao Ku 1
Yunhong Hu 1
Abhisek Kundu 1
Bin Liu 1
Antonio Ortega 1
Mohsen Bayati 1
Pavel Senin 1
Peilin Zhao 1
Raymond Wong 1
Ada Fu 1
Li Zheng 1
Hongxia Yang 1
Jiongqian Liang 1
Noman Mohammed 1
Chao Liu 1
Jaideep Vaidya 1
Collin Stultz 1
Boleslaw Szymanski 1
Maguelonne Teisseire 1
Paolo Boldi 1
Lini Thomas 1
Sachindra Joshi 1
Yixin Chen 1
Xuanhong Dang 1
Tharam Dillon 1
Shumo Chu 1
Luigi Pontieri 1
Francesco Bonchi 1
Sunil Vadera 1
Bingrong Lin 1
Kasim Candan 1
S Upham 1
Thomas Porta 1
Hongzhi Yin 1
Dora Erdős 1
Joydeep Ghosh 1
Kaiyuan Zhang 1
Ming Li 1
Jeffrey Erman 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
Lei Zhang 1
Binbin Lin 1
Christo Wilson 1
Ben Zhao 1
Crystal Chen 1
Susan Frankenstein 1
Feng Tian 1
Jaegul Choo 1
Evangelos Milios 1
Stephen Brooks 1
Juanzi Li 1
Javier Barria 1
Zhishan Guo 1
Bo Liu 1
Yunsing Koh 1
Silei Xu 1
Leonid Hrebien 1
Pei Yang 1
Li Li 1
Denian Yang 1
Ashton Anderson 1
Qing He 1
Sendhil Mullainathan 1
Kai Zhang 1
Christos Anagnostopoulos 1
Yue Wu 1
Kun He 1
Fengyu Qiu 1
Edward Chang 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
Rezwan Ahmed 1
Wei Wei 1
Duygu Ucar 1
Mustafa Bilgic 1
Ben Kao 1
David Cheung 1
Muna Al-Razgan 1
Johannes Gehrke 1
Martin Rosvall 1
Guoqing Chen 1
Lina Yao 1
Zhao Kang 1
Xin Jin 1
Tao Yang 1
Polina Rozenshtein 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
Nitesh Chawla 1
Yasser Altowim 1
Dmitri Kalashnikov 1
Pei Yang 1
Kun Liu 1
Duo Zhang 1
Dmitry Pavlov 1
Raymond Ng 1
Piotr Indyk 1
Christopher Carothers 1
Anne Laurent 1
Satyanarayana Valluri 1
Ashish Verma 1
Li Tu 1
Jérémy Besson 1
Raghu Ramakrishnan 1
Rong Ge 1
Byronju Gao 1
Saharon Rosset 1
Claudia Perlich 1
Tuannhon Dang 1
Seekiong Ng 1
Chengkai Li 1
Ramana Kompella 1
Vasileios Kandylas 1
Salvatore Ruggieri 1
Jing Zhang 1
Xiaojun Chang* 1
Rodrigo Alves 1

Affiliation Paper Counts
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
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
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
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
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
University of Notre Dame 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
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
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
Northeastern University 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
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
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
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
Facebook, Inc. 3
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
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
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
Google Inc. 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
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
Purdue 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
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
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 50

ACM Transactions on Knowledge Discovery from Data (TKDD)

Volume 12 Issue 4, April 2018  Issue-in-Progress
Volume 12 Issue 3, April 2018  Issue-in-Progress
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
Volume 11 Issue 3, April 2017

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