ACM Transactions on

Knowledge Discovery from Data (TKDD)

Latest Articles

Introduction to Special Issue on the Best Papers from KDD 2016

This issue contains the best papers from the ACM KDD Conference 2016. As is customary at KDD, special issue papers are invited only from the research... (more)

Ranking Causal Anomalies for System Fault Diagnosis via Temporal and Dynamical Analysis on Vanishing Correlations

Detecting system anomalies is an important problem in many fields such as security, fault... (more)

comeNgo: A Dynamic Model for Social Group Evolution

How do social groups, such as Facebook groups and Wechat groups, dynamically evolve over time? How do people join the social groups, uniformly or with burst? What is the pattern of people quitting from groups? Is there a simple universal model to depict the come-and-go patterns of various groups? In this article, we examine temporal evolution... (more)

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network... (more)

TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size

“Ogni lassada xe persa.”1-- Proverb from Trieste, Italy. We present trièst, a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local (i.e., incident to each vertex) number of triangles in a fully dynamic graph represented as an adversarial stream of edge... (more)

Graph-Based Fraud Detection in the Face of Camouflage

Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing... (more)

Assessing Human Error Against a Benchmark of Perfection

An increasing number of domains are providing us with detailed trace data on human decisions in settings where we can evaluate the quality of these... (more)

Discovering Conditional Matching Rules

Matching dependencies (MDs) have recently been proposed to make data dependencies tolerant to various information representations, and found useful in data quality applications such as record matching. Instead of the strict equality function used in traditional dependency syntax (e.g., functional dependencies), MDs specify constraints based on... (more)

Query-Driven Learning for Predictive Analytics of Data Subspace Cardinality

Fundamental to many predictive analytics tasks is the ability to estimate the cardinality (number of data items) of multi-dimensional data subspaces,... (more)

Large-Scale Online Feature Selection for Ultra-High Dimensional Sparse Data

Feature selection (FS) is an important technique in machine learning and data mining, especially for large-scale high-dimensional data. Most existing... (more)


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
Community Detection Using Diffusion Information

Community detection in social networks has become a popular topic of research during the last decade. There exist a variety of algorithms for modularizing the network graph into different communities. However, they mostly assume that partial or complete information of the network graphs are available which is not feasible in many cases. In this paper, we focus on detecting communities by exploiting their diffusion information. We utilize the Conditional Random Field (CRF) to model the behavior of diffusion process. The proposed method (CoDi) does not require any prior knowledge of the network structure or specific properties of communities. Furthermore, in contrast to the structure based community detection methods, this method is able to identify the hidden communities. The experimental results indicate considerable improvements in detecting communities based on accuracy, scalability and real cascade information measures.

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

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

Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although social matrix factorization (Social MF) and topic matrix factorization (Topic MF) successfully exploit social relations and item reviews, respectively; both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model MR3 to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.

Systematic Review of Clustering high-dimensional and Large Data Sets

Technological advancement has enabled us to store and process huge amount of data in relatively short spans of time. Nature of data is rapidly increasing its dimensionality to become multi and high dimensional. There is an immediate need to expand our focus to include analysis of high dimensional data and large data sets. Data analysis is becoming a mammoth task due to incremental increase in data volume and complexity in terms of heterogony of data. It is due to this dynamic comput- ing environment that the existing techniques either need to be modified or discarded to handle new data in multiple high dimensions. Data clustering is a tool that is used in many disciplines, including data mining, so that meaningful knowledge can be extracted from seemingly unstructured data. The major aim is to understand the problem of clustering and various approaches addressing this problem. This paper discusses the process of clustering from both micro (data treating) and macro (Overall Clustering Process) views. Various distance and similarity measures, which form the cornerstone of effective data clustering are also identified. Further, an in-depth analysis of different clustering approaches focused on data mining, dealing with large-scale data sets is given. These approaches are also comprehensively compared to bring out a clear differentiation among them. This paper also surveys the problem of high dimensional data and the existing approaches, which helps to make it more negotiable. It also explores the latest trends in cluster analysis and the real life applications of this concept. This survey is exhaustive as it tries to cover all the aspects of clustering in the field of data mining.

Profit Maximization with Sufficient Customer Satisfactions

In many commercial campaigns, we observe that there exists a trade-off between the number of customers satisfied by the company and the profit gained. Merely satisfying as many customers as possible or maximizing the profit is not desirable. To this end, in this paper, we propose a new problem called k-Satisfiability Assignment for Maximizing the Profit (k-SAMP) where k is a user parameter and a non-negative integer. Given a set P of products and a set O of customers, k-SAMP is to find an assignment between P and O such that at least k customers are satisfied in the assignment and the profit incurred by this assignment is maximized. Although we find that this problem is closely related to two classic computer science problems, namely maximum weight matching and maximum matching, the techniques developed for these classic problems cannot be adapted to our k-SAMP problem. In this work, we design a novel algorithm called Adjust for the k-SAMP problem. Given an assignment A, Adjust iteratively increases the profit of A by adjusting some appropriate matches in A while keeping at least k customers satisfied in A. We prove that Adjust returns a global optimum. Extensive experiments were conducted which verified the efficiency of Adjust.

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

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

Fast, Accurate and Flexible Algorithms for Dense Subtensor Mining

Given a large-scale and high-order tensor, how can we find dense blocks in it? Can we find them in near-linear time but with quality guarantees? Extensive previous work has shown that dense blocks in tensors as well as graphs indicate anomalous or fraudulent behavior (e.g., lockstep behavior in social networks). However, available methods for detecting such dense blocks are not satisfactory in terms of speed, accuracy, and flexibility. In this work, we propose M-Zoom and M-Biz, fast and accurate algorithms for finding dense blocks in tensors, which work with various density measures. M-Zoom and M-Biz guarantee a lower bound on the density and local optimality of the blocks they find respectively, and are complementary and combinable. Specifically, our two approaches are: (1) Scalable: scale linearly with all aspects of tensors and is up to 114X faster than state-of-the-art methods with similar accuracy, (2) Provably accurate: guarantee a lower bound on the density and the local optimality of the blocks they find, (3) Flexible: support multi-block detection and size bounds as well as diverse density measures, and (4) Effective: successfully detected edit wars and bot activities in Wikipedia, and spotted network attacks from a TCP dump with near-perfect accuracy (AUC=0.98).

Prioritized Relationship Analysis in Heterogeneous Information Networks

An increasing number of applications are modeled and analyzed in network form, where nodes represent entities of interest and edges represent interactions or relationships between entities. Commonly, such relationship analysis tools assume homogeneity in both node type and edge type. Recent research has sought to redress the assumption of homogeneity and focused on mining heterogeneous information networks (HINs) where both nodes and edges can be of different types. Building on such efforts, in this work we articulate a novel approach for mining relationships across entities in such networks while accounting for user preference over relationship type and interestingness metric. We formalize the problem as a top-k lightest paths problem, contextualized in a real-world communication network, and seek to find the k most interesting path instances matching the preferred relationship type. Our solution, PROphetic HEuristic Algorithm for Path Searching (PRO-HEAPS), leverages a combination of novel graph preprocessing techniques, well-designed heuristics and the venerable A* search algorithm. We run our algorithm on real-world large-scale graphs and show that our algorithm significantly outperforms a wide variety of baseline approaches with speedups as large as 100X. To widen the range of applications, we also extend PRO-HEAPS to (i) support relationship analysis between two groups of entities and (ii) allow pattern path in the query to contain logical statements with operators AND, OR, NOT and wild-card ".". We run experiments using this generalized version of PRO-HEAPS and demonstrate that the advantage of PRO-HEAPS becomes even more pronounced for these general cases. Furthermore, we conduct a comprehensive analysis to study how the performance of PRO-HEAPS varies with respect to various attributes of the input HIN. We finally conduct a case study to demonstrate valuable applications of our algorithm.

Large-scale Bayesian Probablistic Matrix Factorization with Memo-free Distributed Variational Inference

Bayesian Probabilistic Matrix Factorization (BPMF) is a powerful model in many dyadic data prediction problems, especially the application of Recommender system. However, its poor scalability has limited its wide applications on massive data. Based on the conditional independence property of observed entries in BPMF model, we propose a novel distributed memo-free variational inference method for large-scale matrix factorization problems. Compared with the state-of-the-art methods, the proposed method is favored for several attractive properties. Specifically, it does not require tunning of learning rate carefully, shuffling the training set at each iteration, or storing massive redundant variables, and can introduce new agents into the computations on the fly. We conduct extensive experiments on both synthetic and real-world datasets. The experimental results show that our method can converge significantly faster with better prediction performance than alternative algorithms.

De-anonymizing clustered social networks by percolation graph matching

On-line social networks offer the opportunity to collect a huge amount of valuable information about billions of users. The analysis of this data by service providers and unintended third parties are posing serious treats to user privacy. In particular, recent work has shown that users participating in more than one on-line social network can be identified based only on the structure of their links to other users. An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks. In this paper, we show the crucial role of clustering, which is a relevant feature of social network graphs (and many other systems). Clustering has both the effect of making matching algorithms more prone to errors, and the potential to greatly reduce the number of seeds needed to trigger percolation. We show these facts by considering a fairly general class of random geometric graphs with variable clustering level. We assume that seeds can be identified in particular sub-regions of the network graph, while no a-priori knowledge about the location of the other nodes is required. Under these conditions, we show how clever algorithms can achieve surprisingly good performance while limiting the number of matching errors.

Enhancing Reputation via Price Discounts in E-Commerce Systems: A Data Driven Approach

Reputation systems have become an indispensable component of modern E-commerce systems, as they help buyers make informed decisions in choosing trustworthy sellers. To attract buyers and increase transaction volume, sellers need to obtain reasonably high reputations. This process usually takes a substantial amount of time. To accelerate this process, sellers can provide price discounts to attract users, but the underlying difficulty is that sellers have no prior knowledge on buyers preferences over price discounts. In this paper, develop an online algorithm to infer the optimal discount rates from data. We first formulate an optimization framework to select the optimal discount rates given buyers discount preferences, which is a tradeoff between short-term profits and ramp-up time (for reputation). We then derive closed-form optimal discount rates, which give us key insights in applying a stochastic bandits framework to infer optimal discount rates from transaction data with regret upper bounds. We show that the computational complexity of evaluating the performance metrics is infeasibly high, and therefore, we develop efficient randomized algorithms with guaranteed performance to approximate them. Finally, we conduct experiments on an eBays dataset. Experimental results show that our framework can trade 60% of the short-term profits for reducing the ramp-up time by 40%. This reduction in the ramp-up time can increase the sellers long-term profits by at least 20%.

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

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

ProgressER: Adaptive Progressive Approach to Relational Entity Resolution

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

Joint Representation Learning for Location-Based Social Networks with Multi-Grained Sequential Contexts

This paper studies the problem of learning embedding representations for Location-Based Social Networks (LBSN), which is useful in many tasks such as location recommendation and link prediction. Existing network embedding methods mainly focus on capturing topology patterns reflected in social connections, hence, the important data type, \ie check-in sequences, cannot be modeled. In this paper, we propose a representation learning method for LBSNs called as \textbf{JRLM}, which jointly model both social connections and check-in sequences. To capture sequential relatedness, JRLM characterizes two levels of sequential contexts, namely fine-grained and coarse-grained contexts. We present a learning algorithm tailored to the hierarchial architecture of the proposed model. We conduct extensive experiments on two important applications using real-world datasets. The experimental results demonstrate the superiority of our model. The proposed model can generate representations for both users and locations in the same embedding space, which can be exploited in multiple LBSN tasks.

Tied Kronecker Product Graph Models to Capture Variance in Network Populations

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

Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization

Given a neighborhood in a graph with node attributes (e.g., a community, cluster, or group of connected nodes), how can we quantify its quality? Existing measures either only consider the connectedness of the nodes inside the neighborhood and ignore the cross-edges at the boundary (e.g., density) or only quantify the structure of the neighborhood and ignore the attributes (e.g., conductance). In this work, we first introduce a new measure to quantify the normality of an attributed neighborhood. Our normality measure carefully utilizes structure and attributes together to quantify both the internal consistency and external separability. We then formulate an objective function to automatically infer a few attributes (called the neighborhood focus) and respective attribute weights, so as to maximize the normality score of a neighborhood. Most notably, unlike many other approaches, our measure allows for many cross-edges as long as they can be exonerated; i.e., either (i) are expected under a null model, and/or (ii) their boundary nodes do not exhibit the focus attributes. Finally, we propose AMEN, an algorithm that simultaneously discovers the neighborhoods and their respective focus in a given graph, with a goal to maximize the total normality. Neighborhoods for which a focus that yields high normality cannot be found are considered low quality or anomalous. As the experiments on real-world attributed graphs show, AMEN effectively finds anomalous neighborhoods and outperforms several existing measures and methods, such as conductance, density, OddBall, and SODA.

Will Triadic Closure Strengthen Ties in Social Networks?

The social triada group of three peopleis one of the simplest and most fundamental social groups. Extensive network and social theories have been developed to understand the triadic structure, such as triadic closure and social balance. Over the course of a triadic closurethe transition from two ties to three among three users, the strength dynamics of its social ties, however, are much less well understood. Using two dynamic networks from social media and mobile communication, we examine how the formation of the third tie in a triad affects the strength of the existing two ties. Surprisingly, we find that in about 80% social triads, the strength of the first two ties are weakened although averagely the tie strength in the two networks maintains an increasing or stable trend. We discover that 1) the decrease in tie strength among three males is more sharply than that among females, and 2) the tie strength between celebrities are more likely to be weakened as the closure of a triad than those between ordinary people. Further, we formalize a triadic tie strength dynamics prediction problem to infer whether social ties of a triad will become weakened after its closure. We propose a TRIST methoda kernel density estimation (KDE) based graphical modelto solve the problem by incorporating user demographics, temporal effects, and structural information. Extensive experiments demonstrate that TRIST offers a greater than 82% potential predictability for inferring triadic tie strength dynamics in both networks. The leveraging of the kernel density estimation and structural correlations enables TRIST to outperform baselines by up to 30% in terms of F1-score.

GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams

Designing adaptive classifiers for an evolving data stream is a challenging task due to its size and dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is one of the well-known solutions. It is possible that a subset of classifiers in the ensemble outperforms others in a time-varying fashion. However, optimum weight assignment for component classifiers is a problem which is not yet fully addressed in online evolving environments. We propose a novel data stream ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble (GOOWE), which assigns optimum weights to the component classifiers using a sliding window containing the most recent data instances. We map vote scores of individual classifiers and true class labels into a spatial environment. Based on the Euclidean distance between vote scores and ideal-points, and using the linear least squares (LSQ) solution, we present a novel dynamic and online weighting approach. While LSQ is used for batch mode ensemble classifiers, it is the first time that we adapt and use it for online environments by providing a spatial modeling of online ensembles. In order to show the robustness of the proposed algorithm, we use real-world datasets and synthetic data generators using the MOA libraries. We compare our results with 8 state-of-the-art ensemble classifiers in a comprehensive experimental environment. Our experiments show that GOOWE provides improved reactions to different types of concept drift compared to our baselines. The statistical tests indicate a significant improvement in accuracy, with conservative time and memory requirements.

G-RoI: Automatic Region-of-Interest detection driven by geotagged social media data

Geotagged data gathered from social media can be used to discover interesting locations visited by users called Places-of-Interest (PoIs). Since a PoI is generally identified by the geographical coordinates of a single point, it is hard to match it with user trajectories. Therefore, it is useful to define an area, called Region-of-Interest (RoI), to represent the boundaries of the PoIs area. RoI mining techniques are aimed at discovering Regions-of-Interest from PoIs and other data. Existing RoI mining techniques are based on three main approaches: predefined shapes, density-based clustering and grid-based aggregation. This paper proposes G-RoI, a novel RoI mining technique that exploits the indications contained in geotagged social media items to discover RoIs with a high accuracy. Experiments performed over a set of PoIs in Rome and Paris using social media geotagged data, demonstrate that G-RoI in most cases achieves better results than existing techniques. In particular, the mean F1 score is 0.34 higher than that obtained with the well-known DBSCAN algorithm in Rome RoIs and 0.23 higher in Paris RoIs.


Publication Years 2007-2017
Publication Count 299
Citation Count 2819
Available for Download 299
Downloads (6 weeks) 3072
Downloads (12 Months) 28238
Downloads (cumulative) 209797
Average downloads per article 702
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)
Jie Tang ACM Senior Member (2017)
Donald F Towsley ACM Fellows (1997)
Jeffrey D Ullman ACM Karl V. Karlstrom Outstanding Educator Award (1997)
ACM Fellows (1995)
Eli Upfal ACM Fellows (2005)
Limsoon Wong ACM Fellows (2013)
Hui Xiong ACM Distinguished Member (2014)
ACM Senior Member (2010)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)
Philip S Yu ACM Fellows (1997)
Mohammed Zaki ACM Distinguished Member (2010)
Ben Yanbin Zhao ACM Distinguished Member (2015)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)
Zhi-Hua Zhou ACM Fellows (2016)
ACM Distinguished Member (2013)
ACM Senior Member (2011)
Zhi-Hua Zhou ACM Fellows (2016)
ACM Distinguished Member (2013)
ACM Senior Member (2011)

First Name Last Name Paper Counts
Christos Faloutsos 15
Hui Xiong 7
Jieping Ye 7
Jian Pei 5
Aristides Gionis 5
Tao Li 5
Philip YU 4
Zhiwen Yu 4
Zhihua Zhou 4
Shenghuo Zhu 4
Heng Huang 4
John Lui 4
Feiping Nie 4
Bin Guo 4
Huan Liu 4
Hong Cheng 4
Christopher Jermaine 4
John Hopcroft 3
Guofei Jiang 3
Lise Getoor 3
Hanghang Tong 3
Jure Leskovec 3
Malik Magdon-Ismail 3
Xiaoli Fern 3
Qi Liu 3
Enhong Chen 3
Mingsyan Chen 3
Jilles Vreeken 3
Yun Chi 3
Evimaria Terzi 3
Yasushi Sakurai 3
Yihong Gong 3
Lei Tang 3
Dingding Wang 3
Fabio Fassetti 3
Chengqi Zhang 3
Fabrizio Angiulli 3
Hari Sundaram 2
Mohamed Bouguessa 2
Spiros Papadimitriou 2
Joydeep Ghosh 2
Wei Fan 2
Dino Pedreschi 2
Charu Aggarwal 2
Jon Kleinberg 2
Neil Shah 2
Eli Upfal 2
Wei Wang 2
Kristina Lerman 2
Pinghui Wang 2
Hao Huang 2
Jiawei Han 2
Hong Qin 2
Yehuda Koren 2
Chen Chen 2
Heikki Mannila 2
Panayiotis Tsaparas 2
Zhu Wang 2
Jianhui Chen 2
Yangqiu Song 2
Yu Zhang 2
Arthur Zimek 2
Michalis Vazirgiannis 2
Junzhou Zhao 2
Xiaohong Guan 2
Geoffrey Webb 2
Indrajit Bhattacharya 2
Panagis Magdalinos 2
Jin Huang 2
Xiao Yu 2
Wei Cheng 2
Peng Cui 2
Qiang Yang 2
Yanjie Fu 2
Charles Ling 2
Andrea Esuli 2
Nikolaj Tatti 2
Petros Drineas 2
JiLei Tian 2
Ping Luo 2
B Prakash 2
Yuru Lin 2
Shinjae Yoo 2
Ian Davidson 2
Antonella Guzzo 2
Steven Hoi 2
Jiawei Han 2
Ruoming Jin 2
Antônio Loureiro 2
Lei Chen 2
Daniel Kifer 2
Alex Beutel 2
Xiang Zhang 2
Yan Liu 2
Fabrizio Sebastiani 2
Laks Lakshmanan 2
Jimeng Sun 2
Don Towsley 2
Rita Chattopadhyay 2
Sucheta Soundarajan 2
Matteo Riondato 2
Yong Ge 2
Xianchao Zhang 2
Dacheng Tao 2
Wei Ding 2
Belle Tseng 2
Vivekanand Gopalkrishnan 2
Jie Tang 2
Eugene Agichtein 2
Kui Yu 2
Christopher Leckie 2
Carlotta Domeniconi 2
Sanjay Ranka 2
Jiliang Tang 2
Dantong Yu 2
Charalampos Tsourakakis 2
Jirong Wen 2
Martin Ester 2
Srinivasan Parthasarathy 2
Ahmed El-Mahdy 1
Jeffreyxu Yu 1
Soroush Vosoughi 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
Hamid Rabiee 1
Shifeng Weng 1
Maryam Tahani 1
Ying Wei 1
Yubao Wu 1
Junming Shao 1
Yllka Velaj 1
Xiaojun Chang 1
Lars Schmidt-Thieme 1
Jun Yan 1
Marimuthu Palaniswami 1
James Bezdek 1
Jayavardhana Gubbi 1
Michael Lyu 1
Nicholas Sidiropoulos 1
Dityan Yeung 1
Evangelos Papalexakis 1
Davoud Moulavi 1
George Karypis 1
Jilei Tian 1
Fei Yi 1
Ting Guo 1
Jia Wu 1
Xingquan Zhu 1
Xun Tang 1
Jevin West 1
Biao Xiang 1
Yi Zheng 1
Koji Hino 1
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Yu Zheng 1
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Xiao Jiang 1
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Chun Li 1
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Bin Li 1
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Dick Epema 1
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Min Wang 1
Marc Maier 1
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MingXi Wu 1
John Canny 1
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Ye Chen 1
Yeowwei Choong 1
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Bo Long 1
Kuan Zhang 1
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Min Wang 1
Claudio Schifanella 1
Shuiwang Ji 1
Ali Pınar 1
Ling Chen 1
Yang Liu 1
Michail Vlachos 1
Chunxiao Xing 1
Dechuan Zhan 1
Ruggero Pensa 1
Bryan Hooi 1
Tetsuji Ogawa 1
Yuto Yamaguchi 1
Shaoxu Song 1
Zijun Yao 1
Weiming Hu 1
Maoying Qiao 1
Wei Bian 1
Ying Jin 1
Hiroshi Mamitsuka 1
Carlos Lorenzetti 1
Dan Roth 1
Thomas Reichherzer 1
Ephraim Korach 1
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Kai Zheng 1
Zhongyuan Wang 1
Allon Percus 1
Xunhua Guo 1
Xutong Liu 1
Yencheng Lu 1
Xue Li 1
Guodong Long 1
Ümit Çatalyürek 1
Jie Cheng 1
Sitaram Asur 1
Jerry Kiernan 1
Kevin Yip 1
Wei Zheng 1
Zhenxing Wang 1
Ravi Konuru 1
Baoxing Huai 1
Hengshu Zhu 1
Nick Street 1
Pritam Gundecha 1
Edward Wild 1
Fan Guo 1
Murat Kantarcıoğlu 1
John Guttag 1
Marc Plantevit 1
Jinlin Chen 1
Binay Bhattacharya 1
Alin Dobra 1
Shantanu Godbole 1
Bin Zhou 1
Anushka Anand 1
Yicheng Tu 1
Dan Simovici 1
Hao Wang 1
Madhav Jha 1
Siddharth Gopal 1
Jose Hern´ndez-Orallo 1
Rainer Gemulla 1
Saurabh Paul 1
Guangtao Wang 1
Xueying Zhang 1
Yiping Ke 1
Evrim Acar 1
Yang Zhou 1
Ben London 1
Joseph Ruiz Md 1
Masahiro Kimura 1
Alexander Ihler 1
Kaiwei Chang 1
Forrest Briggs 1
Gustavo Batista 1
Qiang Zhu 1
Philip Yu 1
Jure Leskovec 1
Maya Bercovitch 1
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Michalis Faloutsos 1
Shebuti Rayana 1
Liming Chen 1
Hongxia Yang 1
Haoda Fu 1
Dawei Zhou 1
Jingrui He 1
Stefan Kramer 1
Huaimin Wang 1
Qiang You 1
Luke McDowell 1
Miao Tian 1
Naren Ramakrishnan 1
Minoru Kanehisa 1
Qi Tian 1
Jennifer Neary 1
Shantanu Sharma 1
Teresa Tjahja 1
Yuanli Pei 1
Wenchih Peng 1
Zekai Gao 1
Sutharshan Rajasegarar 1
Jeffrey Chan 1
Laura Smith 1
Jin Zhang 1
Irwin King 1
Ling Liu 1
Hua Wang 1
Alice Leung 1
Renato Assunção 1
Subhabrata Sen 1
Dino Ienco 1
Rosa Meo 1
Lei Ying 1
Yu Shi 1
Tianyang Zhang 1
Shiqiang Yang 1
Agma Traina 1
Mostafa Mohsenvand 1
Pauli Miettinen 1
Eduardo Hruschka 1
Hongliang Fei 1
Jun Huan 1
Carlos Garcia-Alvarado 1
Ana Appel 1
Jeffreyxu Yu 1
Yashu Liu 1
Zhen Guo 1
Waynexin Zhao 1
Andrew Mehler 1
Faming Lu 1
Stephen North 1
Chojui Hsieh 1
Chihjen Lin 1
Seungil Huh 1
Zheng Wang 1
Thanawin Rakthanmanon 1
Jesin Zakaria 1
Kedar Bellare 1
Brandon Norick 1
Ming Ji 1
Yuval Elovici 1
Ming Lin 1
Changshui Zhang 1
Sri Ravana 1
Shiqiang Yang 1
Wangchien Lee 1
Zoran Obradović 1
Qinli Yang 1
Xiang Li 1
Josif Grabocka 1
Nicolas Schilling 1
Richard Xu 1
David Aha 1
Sougata Mukherjea 1
Huilei He 1
Guannan Liu 1
Liang Wang 1
Kimon Fountoulakis 1
Shanshan Feng 1
Fei Zou 1
Virgílio Almeida 1
Christos Faloutsos 1
Laiwan Chan 1
Nitin Agarwal 1
S Muthukrishnan 1
Amin Saberi 1
Alexandru Iosup 1
Anthony Tung 1
Kunta Chuang 1
Adelelu Jia 1
Aniket Chakrabarti 1
Reza Zafarani 1
Saurabh Kataria 1
Matthew Rattigan 1
Geoffrey Barbier 1
Limin Yao 1
Cheukkwong Lee 1
Olvi Mangasarian 1
Chris Clifton 1
Mohammed Zaki 1
Jennifer Dy 1
Loïc Cerf 1
Henry Tan 1
Shaojun Wang 1
Gianluigi Greco 1
Francesco Gullo 1
Guimei Liu 1
Min Ding 1
Gensheng Zhang 1
Jennifer Neville 1
Yiming Yang 1
Vassilios Vassiliadis 1
Kaiming Ting 1
Kijung Shin 1
Lorenzo De Stefani 1
Alessandro Epasto 1
Caetanotraina Jr 1
Christophe Giraud-Carrier 1
Oualid Boutemine 1
Ayan Acharya 1
Sreangsu Acharyya 1
Ashwin Ram 1
Animesh Mukherjee 1
Sriram Srinivasan 1
Niloy Ganguly 1
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Lingjyh Chen 1
Linhong Zhu 1
Makoto Yamada 1
Guoqing Chen 1
Zhanpeng Fang 1
Jing Peng 1
Yang Zhou 1
Xinjiang Lu 1
Kamer Kaya 1
Quan Sheng 1
Qiang Cheng 1
Maha Alabduljalil 1
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Dengyong Zhou 1
Jian Cao 1
Jie Wang 1
Shiyou Qian 1
Ming Zhang 1
Biru Dai 1
Divesh Srivastava 1
Liang Hong 1
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Hungleng Chen 1
Venu Satuluri 1
Rose Yu 1
Yao Zhang 1
Paul Castro 1
Hunghsuan Chen 1
Aisling Kelliher 1
Lian Duan 1
Bruno Ribeiro 1
Siyuan Liu 1
Shengrui Wang 1
Anon Plangprasopchok 1
Patrick Hung 1
Ganesh Ramesh 1
A Patterson 1
Manolis Kellis 1
Beechung Chen 1
Carlos Castillo 1
Amit Dhurandhar 1
Fedja Hadzic 1
Tianbing Xu 1
Arnold Boedihardjo 1
Changtien Lu 1
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Aditya Menon 1
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Edward Chang 1
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Sigal Sina 1
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Tingting Gao 1
Dityan Yeung 1
Longjie Li 1
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Yada Zhu 1
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Lei Ma 1
Xing Yong 1
T Murali 1
Kiyoko Aoki-Kinoshita 1
Sudhir Kumar 1
Ravi Janardan 1
Ming Zhang 1
Shlomi Dolev 1
Fang Wang 1
Haixun Wang 1
Zhirui Hu 1
Dheeraj Kumar 1
Yao Wu 1
Elizabeth Chang 1
Sanmay Das 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
Maria Sapino 1
Shipeng Yu 1
Zhiting Hu 1
Pedro Melo 1
Yuan Jiang 1
Jingchao Ni 1
Alceu Costa 1
Yihan Wang 1
Qinbao Song 1
Michele Coscia 1
Yi Wang 1
Jaideep Srivastava 1
Charles Elkan 1
João Gama 1
Carlos Guestrin 1
Tomoharu Iwata 1
Naonori Ueda 1
Qi Lou 1
Wei Fan 1
Xifeng Yan 1
Julian McAuley 1
Feiyu Xiong 1
Fei Wang 1
Shiqiang Tao 1
Guoqiang Zhang 1
Bertil Schmidt 1
Yi Yang 1
Quanzeng You 1
Tao Mei 1
Kosuke Hashimoto 1
Nobuhisa Ueda 1
Bin Liu 1
Antonio Ortega 1
Jie Tang 1
Haiqin Yang 1
Aparna Varde 1
Ricardo Campello 1
Shuhui Wang 1
Dandan Qiao 1
Tiancheng Lou 1
Guna Seetharaman 1
Giacomo Berardi 1
Xiaotong Zhang 1
Han Liu 1
Feng Chen 1
Kathleen Carley 1
Eugenia Kontopoulou 1
Xiaodan Song 1
Yasuhiro Fujiwara 1
Wei Wang 1
ChienWei Chen 1
Weiyin Loh 1
John Salerno 1
Nitin Kumar 1
Flip Korn 1
Ying Wang 1
Siqi Shen 1
Xinran He 1
Lei Li 1
Ke Wang 1
Chris Ding 1
Jing Zhang 1
Benoît Dumoulin 1
Xiuyao Song 1
John Gums 1
Yin Zhang 1
Jude Shavlik 1
Zhongfei Zhang 1
Yunxin Zhao 1
Sibel Adalı 1
Xiaohui Lu 1
Qian Sun 1
Domenico Saccà 1
Francesco Lupia 1
Nima Mirbakhsh 1
Antti Ukkonen 1
Xindong Wu 1
Zheng Wang 1
Bin Cui 1
Johannes Schneider 1
Juanzi Li 1
Patrick Haffner 1
Zhili Zhang 1
Qingyan Yang 1
Lei Xie 1
Hyunah Song 1
Qiang Qu 1
Can Chen 1
Erik Saule 1
Tao Ku 1
Yunhong Hu 1
Seunghee Bae 1
Abhisek Kundu 1
Pedro Vaz De Melo 1
Michael Houle 1
Jeffrey Chan 1
Dimitrios Gunopulos 1
Daxin Jiang 1
Mohsen Bayati 1
Peilin Zhao 1
Muna Al-Razgan 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
Boleslaw Szymanski 1
Maguelonne Teisseire 1
Tharam Dillon 1
Paolo Boldi 1
Lini Thomas 1
Sachindra Joshi 1
Yixin Chen 1
Xuanhong Dang 1
Shumo Chu 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
Qing He 1
Ashton Anderson 1
Sendhil Mullainathan 1
Kai Zhang 1
Christos Anagnostopoulos 1
Haifeng Chen 1
Xiang Zhang 1
Hao Ye 1
Peter Triantafillou 1
Nenghai Yu 1
Scott Burton 1
Christos Boutsidis 1
Bingsheng Wang 1
Hui Ke 1
Tamara Kolda 1
Jie Wang 1
Karthik Subbian 1
João Duarte 1
Yulan He 1
Galileo Namata 1
John Frenzel MD 1
Hua Duan 1
Joshua Vogelstein 1
Yandong Liu 1
Qiaozhu Mei 1
Takeshi Yamada 1
Suresh Iyengar 1
Jiawei Han 1
Ashwin Machanavajjhala 1
Ali Hemmatyar 1
Erheng Zhong 1
Wei Fan 1
Yi Zhen 1
Meng Jiang 1
Chihya Shen 1
Zhitao Wang 1
Jingrui He 1
Beilun Wang 1
Yue Wu 1
Dora Erdős 1
Joydeep Ghosh 1
Kaiyuan Zhang 1
Carlos Ordonez 1
Fosca Giannotti 1
James Cheng 1
Peter Christen 1
U Kang 1
Daniel Dunlavy 1
Christos Doulkeridis 1
David Dominguez-Sal 1
Steven Skiena 1
Danai Koutra 1
Hiroshi Motoda 1
Chris Volinsky 1
Andreas Krause 1
Hsiangfu Yu 1
Aditya Parameswaran 1
Binbin Lin 1
Johannes Gehrke 1
Christo Wilson 1
Ben Zhao 1
Zhishan Guo 1
Yunsing Koh 1
Silei Xu 1
Bo Liu 1
Li Li 1
Denian Yang 1
Leonid Hrebien 1
Pei Yang 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
Filippo Menczer 1
Ana Maguitman 1
Foto Afrati 1
Rómer Rosales 1
Xiaofang Zhou 1
Xindong Wu 1
Fangtao Li 1
Junjie Wu 1
Wei Fan 1
Rezwan Ahmed 1
Guoqing Chen 1
Xiaojun Chang* 1
Lina Yao 1
Wei Wei 1
Zhao Kang 1
Xin Jin 1
Tao Yang 1
Martin Rosvall 1
Polina Rozenshtein 1
Duygu Ucar 1
Mustafa Bilgic 1
Ben Kao 1
David Cheung 1
Cheng Zeng 1
Atreya Srivathsan 1
Tong Sun 1
Yanchi Liu 1
Songhua Xu 1
Kun Liu 1
Duo Zhang 1
Dmitry Pavlov 1
Raymond Ng 1
Piotr Indyk 1
Christopher Carothers 1
Anne Laurent 1
Raghu Ramakrishnan 1
Rong Ge 1
Byronju Gao 1
Satyanarayana Valluri 1
Jérémy Besson 1
Ashish Verma 1
Li Tu 1
Tuannhon Dang 1
Saharon Rosset 1
Claudia Perlich 1
Seekiong Ng 1
Hong Xie 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
Yunfei Lu 1
Wenwu Zhu 1

Affiliation Paper Counts
Lancaster University 1
Osaka University 1
University of Iowa 1
National University of Defense Technology China 1
Wright-Patterson AFB 1
Eli Lilly and Company 1
Swiss Federal Institute of Technology, Zurich 1
Lawrence Livermore National Laboratory 1
University of Rochester 1
Naval Research Laboratory 1
Stevens Institute of Technology 1
Jerusalem College of Technology 1
National Taiwan University of Science and Technology 1
Oracle Corporation 1
Lanzhou University 1
University of New South Wales 1
Northeastern University 1
Research Organization of Information and Systems National Institute of Informatics 1
University of Malaya 1
University of Milan 1
Temple University 1
Syracuse University 1
Umea University 1
Curtin University of Technology, Perth 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
California State University Fullerton 1
University of Michigan 1
Anhui University 1
University of Ontario Institute of Technology 1
Universite de Cergy-Pontoise 1
National Technical University of Athens 1
Princeton University 1
Claremont Graduate University 1
Queens College, City University of New York 1
Iowa State University 1
University of Arkansas - Fayetteville 1
Yale University 1
University of Auckland 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
Nanjing University of Science and Technology 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
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
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
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
Max Planck Institute for Informatics 2
University of Glasgow 2
Hefei University of Technology 2
Zhejiang University 2
Institute of High Performance Computing, Singapore 2
Johns Hopkins University 2
University of Electronic Science and Technology of China 2
Tel Aviv University 2
University of Minnesota System 2
University of Houston 2
The University of Hong Kong 2
Brigham Young University 2
The University of North Carolina at Charlotte 2
Harvard University 2
Montclair State University 2
South National University 2
Hong Kong Baptist University 2
University of California, Davis 2
Drexel 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 California, Berkeley 2
University of Tokyo 2
University Michigan Ann Arbor 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
Qatar Computing Research institute 2
Facebook, Inc. 2
International Institute of Information Technology Hyderabad 3
Shandong University of Science and Technology 3
Bar-Ilan University 3
University of Hildesheim 3
Indian Institute of Technology, Kharagpur 3
University of Pennsylvania 3
University of California, Irvine 3
The University of British Columbia 3
University of Texas M. D. Anderson Cancer Center 3
University of Southern California, Information Sciences Institute 3
University of Kentucky 3
INSA Lyon 3
Academia Sinica Taiwan 3
George Mason University 3
Institute of Automation Chinese Academy of Sciences 3
Xerox Corporation 3
Chinese Academy of Sciences 3
Binghamton University State University of New York 3
Italian National Research Council 3
University of Massachusetts Boston 3
University of Sydney 3
Wuhan University 3
Southern Illinois University at Carbondale 3
University of Alberta 3
University of Queensland 3
Johannes Gutenberg University Mainz 3
The Chinese University of Hong Kong, Shenzhen 3
Emory University 4
Institute for Infocomm Research, A-Star, Singapore 4
Brookhaven National Laboratory 4
Universitat Politecnica de Catalunya 4
The University of Western Ontario 4
IBM Research 4
Brown University 4
University of Antwerp 4
Beihang University 4
AT&T Inc. 4
University of Washington, Seattle 4
National University of Singapore 4
Athens University of Economics and Business 4
Monash University 4
Boston University 4
Shanghai Jiaotong University 4
Microsoft Corporation 4
Sharif University of Technology 4
University of Pisa 4
Yahoo Research Barcelona 4
Case Western Reserve University 5
University of Texas at San Antonio 5
Ohio State University 5
Dalian University of Technology 5
Rice University 5
Google Inc. 5
Sandia National Laboratories, New Mexico 5
University of Turin 5
Renmin University of China 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
Delft University of Technology 6
University of Sao Paulo 6
AT&T Laboratories Florham Park 6
Kyoto University 6
University of Massachusetts Amherst 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
Purdue University 7
University of Florida 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
Aalto University 7
Rutgers University-Newark Campus 8
Pennsylvania State University 8
Nanyang Technological University 8
University of Texas at Austin 8
Oregon State University 8
Xi'an Jiaotong University 8
Microsoft Research 8
Stony Brook University 8
Nanjing University 8
Peking University 9
Florida International University 9
Georgia Institute of Technology 9
Yahoo Research Labs 9
National Taiwan University 10
Stanford University 10
IBM Thomas J. Watson Research Center 10
University of Melbourne 10
Virginia Tech 10
University of Illinois at Chicago 10
Rensselaer Polytechnic Institute 11
Hong Kong University of Science and Technology 12
University of Calabria 12
Cornell University 13
University of Science and Technology of China 13
University of Texas at Arlington 15
Northwestern Polytechnical University China 16
University of Technology Sydney 16
Simon Fraser University 17
Chinese University of Hong Kong 18
University of Illinois at Urbana-Champaign 19
NEC Laboratories America, Inc. 19
Tsinghua University 33
Carnegie Mellon University 41
Arizona State University 48

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