ACM Transactions on

Knowledge Discovery from Data (TKDD)

Latest Articles

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... (more)

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... (more)

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 (IE). One important assumption... (more)

ClassiNet -- Predicting Missing Features for Short-Text Classification

Short and sparse texts such as tweets, search engine snippets, product reviews, and chat messages are abundant on the Web. Classifying such... (more)

Spatio-Temporal Routine Mining on Mobile Phone Data

Mining human behaviors has always been an important subarea of Data Mining. While it provides empirical evidences to psychological/behavioral studies,... (more)

SemRe-Rank: Improving Automatic Term Extraction by Incorporating Semantic Relatedness with Personalised PageRank

Automatic Term Extraction (ATE) 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 there is no existing ATE methods that can consistently outperform others in any domain. This work adopts a... (more)

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... (more)

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... (more)

Cluster’s Quality Evaluation and Selective Clustering Ensemble

Clustering ensemble has drawn much attention in recent years due to its ability to generate a high quality and robust partition result. Weighted... (more)

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

ABPA Ξ AΣ (ABRAXAS): Gnostic word of mystic meaning. 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... (more)

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

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

Large-scale Adversarial Sports Play Retrieval with Learning to Rank

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

Enumerating Trillion Subgraphs On Distributed Systems

How can we find patterns from an enormous graph with billions of vertices and edges? The subgraph enumeration, which is finding patterns from a graph, is an important task for graph data analysis with many applications including analyzing the social network evolution, measuring the significance of motifs in biological networks, observing the dynamics of Internet, etc. Especially, the triangle enumeration, a special case of the subgraph enumeration where the pattern is a triangle, has many applications such as identifying suspicious users in social networks, detecting web spams, and finding communities. However, recent networks are so large that most of the previous algorithms fail to process them. Recently, several MapReduce algorithms have been proposed to address such large networks; however, they suffer from the massive shuffled data resulting in a very long processing time. In this paper, we propose scalable methods for enumerating trillion subgraphs on distributed systems. We first propose PTE (Pre-partitioned Triangle Enumeration), a new distributed algorithm for enumerating triangles in enormous graphs by resolving the structural inefficiency of the previous MapReduce algorithms. PTE enumerates trillions of triangles in a billion scale graph by decreasing three factors: the amount of shuffled data, total work, and network read. We also propose PSE (Pre-partitioned Subgraph Enumeration), a generalized version of PTE for enumerating subgraphs that match an arbitrary query graph. Experimental results show that PTE provides 47 times faster performance than recent distributed algorithms on real-world graphs, and succeeds in enumerating more than 3 trillion triangles on the ClueWeb12 graph with 6.3 billion vertices and 72 billion edges. Furthermore, PSE successfully enumerates 265 trillion clique subgraphs with 4 vertices from a subdomain hyperlink network, showing 49 times faster performance than the state of the art distributed subgraph enumeration algorithm.

Entity Based Query Recommendation for Long-Tail Queries

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

A General Embedding Framework for Heterogeneous Information Learning in Large-scale Networks

Network analysis has been widely applied in many real-world tasks such as gene analysis and targeted marketing. To extract effective features for these analysis tasks, network embedding automatically learns a low-dimensional vector representation for each node, such that the meaningful topological proximity is well preserved. While the embedding algorithms on pure topological structure have attracted considerable attention, in practice, nodes are often abundantly accompanied with other types of meaningful information such as node attributes, second-order proximity, and link directionality. A general framework for incorporating the heterogeneous information into network embedding could be potentially helpful in learning better vector representations. However, it remains a challenging task to jointly embed the geometrical structure and a distinct type of information due to the heterogeneity. In addition, the real-world networks often contain a large number of nodes, which put demands on the scalability of the embedding algorithms. To bridge the gap, in this paper, we propose a general embedding framework named Heterogeneous Information Learning in Large-scale networks (HILL) to accelerate the joint learning. It enables the simultaneous node proximity assessing process to be done in a distributed manner by decomposing the complex modeling and optimization into many simple and independent sub-problems. We validate the significant correlation between the heterogeneous information and topological structure, and illustrate the generalizability of HILL by applying it to perform attributed network embedding and second-order proximity learning. A variation is proposed for link directionality modeling. Experimental results on real-world networks demonstrate the effectiveness and efficiency of HILL.

Sequential Feature Explanations for Anomaly Detection

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

Stability and Robustness in Influence Maximization

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

DIGGER: Detect Similar Groups in Heterogeneous Social Networks

People participate in multiple online social networks, e.g., Facebook, Twitter, and Linkedin, and these social networks with heterogeneous social content and user relationship are named as heterogeneous social networks. Group structure widely exists in heterogeneous social networks, which reveals the evolution of human cooperation. Detecting similar groups in heterogeneous networks has a great significance for many applications, such as recommendation system and spammer detection, using the wealth of group information. Although promising, this novel problem encounters a variety of technical challenges, including incomplete data, high time complexity, and ground truth. To address the research gap and technical challenges, we take advantage of a ratio-cut optimization function to model this novel problem by the linear mixed-effects method and graph spectral theory. Based on this model, we propose an efficient algorithm called \textsc{Digger} to detect the similar groups in the large graphs. \textsc{Digger} consists of three steps, including measuring user similarity, construct a matching graph and detecting similar groups. We adopt several strategies to lower the computational cost and detail the basis of labeling the ground truth. We evaluate the effectiveness and efficiency of our algorithm on five different types of online social networks. The extensive experiments show that our method achieves 0.633, 0.723 and 0.675 in precision, recall and F1-measure, which significantly surpass the state-of-arts by 24.6$\%$, 14.6$\%$ and 19.7$\%$, respectively. The results demonstrate that our proposal can detect similar groups in heterogeneous networks effectively.

Protecting privacy in trajectories with a user-centric approach

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

Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model

Recently latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the sparsity of the rating matrix always limits their performance. Meanwhile, semi-supervised learning (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the smoothness insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this paper, we propose a novel probabilistic chain graph model (CGM) to marry SSL with LFM. The proposed CGM is a combination of Bayesian network and Markov random field. The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constrain between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.

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

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

FrauDetector+: A Graph-Mining Approach for Efficient Fraudulent Phone Call Detection

In recent years, telecommunication fraud is becoming more rampant internationally with the development of modern technology and global communication. Due to the rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with Big Data issues in real-world implementations. While our previous work, FrauDetector, has addressed this problem and achieved some promising results, it can be further enhanced as it only focuses on the fraud detection accuracy while the efficiency and scalability are not on the top priority. Meanwhile, other known approaches for fraudulent call number detection suffer from long training time and/or cannot accurately detect fraudulent phone calls in real time. In this paper, we propose a highly efficient incremental graph-mining-based fraudulent phone call detection approach, namely FrauDetector+, which is able to automatically label fraudulent phone numbers with a fraud tag, a crucial prerequisite for distinguishing fraudulent phone call numbers from the normal ones. FrauDetector+ initially generates smaller, more manageable sub-networks from the original graph and performs a parallelized weighted HITS algorithm for a significant speed acceleration in the graph learning module. It adopts a novel aggregation approach to generate the trust (or experience) value for each phone number (or user) based on their respective local values. After the initial procedure, we can incrementally update the trust (or experience) value for each phone number (or user) while a new fraud phone number is identified. An efficient fraud-centric hash structure is constructed to support fast, real-time detection of fraudulent phone numbers in the detection module. We conduct a comprehensive experimental study based on real datasets collected through an anti-fraud mobile application, Whoscall. The results demonstrate a significantly improved efficiency of our approach compared to FrauDetector and superior performance against other major classifier-based methods.

Coupled Clustering Ensemble by Exploring Data Independence

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


Publication Years 2007-2018
Publication Count 361
Citation Count 3286
Available for Download 361
Downloads (6 weeks) 2910
Downloads (12 Months) 29722
Downloads (cumulative) 229625
Average downloads per article 636
Average citations per article 9
First Name Last Name Award
Foto Afrati ACM Fellows (2014)
Charu Chandra Aggarwal ACM Fellows (2013)
John Canny ACM Doctoral Dissertation Award (1987)
Carlos A. Castillo ACM Senior Member (2014)
Ming-Syan Chen ACM Fellows (2006)
Chris Clifton ACM Distinguished Member (2017)
ACM Senior Member (2006)
Graham R. Cormode ACM Distinguished Member (2013)
Christos Faloutsos ACM Fellows (2010)
Benjamin Fung ACM Senior Member (2013)
Johannes Gehrke ACM Fellows (2014)
Lee Giles ACM Fellows (2006)
John Guttag ACM Fellows (2006)
Jiawei Han ACM Fellows (2003)
John E Hopcroft ACM Karl V. Karlstrom Outstanding Educator Award (2008)
ACM Fellows (1994)
ACM A. M. Turing Award (1986)
Piotr Indyk ACM Fellows (2015)
ACM Paris Kanellakis Theory and Practice Award (2012)
Masaru Kitsuregawa ACM Fellows (2012)
Jon Kleinberg ACM AAAI Allen Newell Award (2014)
ACM Fellows (2013)
ACM Prize in Computing (2008)
Sarit Kraus ACM Fellows (2014)
Hans-Peter Kriegel ACM Fellows (2009)
Laks Lakshmanan ACM Distinguished Member (2016)
Ming Li ACM Fellows (2006)
Chih-Jen Lin ACM Fellows (2015)
ACM Distinguished Member (2011)
ACM Senior Member (2010)
Chang-Tien Lu ACM Distinguished Member (2015)
Tao Mei ACM Distinguished Member (2016)
ACM Senior Member (2012)
Filippo Menczer ACM Distinguished Member (2013)
S. Muthukrishnan ACM Fellows (2010)
Shamkant Navathe ACM Fellows (2014)
Sethuraman Panchanathan ACM Senior Member (2009)
Jian Pei ACM Fellows (2015)
ACM Senior Member (2007)
Ali Pinar ACM Distinguished Member (2015)
ACM Senior Member (2011)
Raghu Ramakrishnan ACM Fellows (2001)
Dan Roth ACM Fellows (2011)
Michael Rung-Tsong Lyu ACM Fellows (2015)
Domenico Sacca ACM Senior Member (2007)
J. Sander ACM Distinguished Member (2015)
Padhraic Smyth ACM Fellows (2013)
Divesh Srivastava ACM Fellows (2011)
John Stasko ACM Distinguished Member (2011)
ACM Senior Member (2011)
Jie Tang ACM Senior Member (2017)
Donald F Towsley ACM Fellows (1997)
Paolo Trunfio ACM Senior Member (2017)
Jeffrey D Ullman ACM Karl V. Karlstrom Outstanding Educator Award (1997)
ACM Fellows (1995)
Eli Upfal ACM Fellows (2005)
Limsoon Wong ACM Fellows (2013)
Hui Xiong ACM Distinguished Member (2014)
ACM Senior Member (2010)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)
Philip S Yu ACM Fellows (1997)
Mohammed Zaki ACM Distinguished Member (2010)
Ben Y. Zhao ACM Distinguished Member (2015)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)
Zhi-Hua Zhou ACM Fellows (2016)
ACM Distinguished Member (2013)
ACM Senior Member (2011)
Zhi-Hua Zhou ACM Fellows (2016)
ACM Distinguished Member (2013)
ACM Senior Member (2011)

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

Affiliation Paper Counts
Innopolis University 1
Hong Kong Red Cross Blood Transfusion Service 1
Naval Research Laboratory 1
Anhui University 1
University of Louisiana at Lafayette 1
National Technical University of Athens 1
Vietnam National University 1
University of New South Wales 1
University of Kuwait 1
City University of Hong Kong 1
Hong Kong Polytechnic University 1
University of Texas at Dallas 1
Lanzhou University 1
INRIA Lorraine 1
Korea University 1
Vilnius University 1
Zhejiang Wanli University 1
National University of Defense Technology China 1
Syracuse University 1
University of Missouri-Columbia 1
Iowa State University 1
Soochow University 1
University of Arkansas - Fayetteville 1
Vanderbilt University 1
RMIT University 1
Universite Claude Bernard Lyon 1 1
Shenyang Institute of Automation Chinese Academy of Sciences 1
University of West Florida 1
National Chiao Tung University Taiwan 1
University of Notre Dame 1
Ryukoku University 1
University of Montpellier 1
Universite de Cergy-Pontoise 1
Liaoning Normal University 1
Air Force Research Laboratory 1
City University of New York 1
University at Buffalo, State University of New York 1
HP Labs 1
University of Ontario Institute of Technology 1
Jerusalem College of Technology 1
Norwegian University of Science and Technology 1
Lancaster University 1
University of Toronto 1
California State University Fullerton 1
IBM, India 1
South China Normal University 1
Yale University 1
University of Michigan 1
Sandia National Laboratories, California 1
Universidad Politecnica de Valencia 1
University of Auckland 1
University of Malaya 1
University of Connecticut 1
Waseda University 1
University of South Florida Tampa 1
Universite Lille 2 Droit et Sante 1
Curtin University of Technology, Perth 1
Queen's University Belfast 1
Aston University 1
Nokia USA 1
Universidad Adolfo Ibanez 1
Claremont Graduate University 1
BBN Technologies 1
Ferdowsi University of Mashhad 1
Valley Laboratory 1
Washington University in St. Louis 1
Macquarie University 1
Sabanci University 1
eBay, Inc. 1
Wright-Patterson AFB 1
Colorado School of Mines 1
University of Arizona 1
University of Florence 1
Princeton University 1
University of Milan 1
Queens College, City University of New York 1
Indian Institute of Science, Bangalore 1
National Central University Taiwan 1
Netherlands Organisation for Applied Scientific Research - TNO 1
National Taiwan University of Science and Technology 1
Griffith University 1
State University of New York at Albany 1
University of Salford 1
MITRE Corporation 1
University of Iowa 1
Southwestern University 1
Oracle Corporation 1
Harvard School of Engineering and Applied Sciences 1
Twitter, Inc. 1
Nanjing University of Aeronautics and Astronautics 1
University of New Mexico 1
Eli Lilly and Company 1
De Montfort University 1
University of Tsukuba 1
Air Force Research Laboratory Information Directorate 1
Siemens USA 1
Oak Ridge National Laboratory 1
Ariel University Center of Samaria 1
US Naval Academy 1
University of Rochester 1
Australian National University 1
University of Vermont 1, Inc. 1
Wright State University 1
Temple University 1
Alexandria University 1
Georgia Tech Research Institute 1
Brigham and Women's Hospital 1
Radboud University Nijmegen 1
John F. Kennedy School of Government 1
University of Gottingen 1
University of Shizuoka 1
Osaka University 1
Umea University 1
Florida Atlantic University 1
Yuncheng University 1
University of Hawaii at Hilo 1
Saarland University 1
Riken 1
Stevens Institute of Technology 1
John Carroll University 1
Industrial Technology Research Institute of Taiwan 2
Zhejiang University 2
South National University 2
IBM Zurich Research Laboratory 2
Brigham Young University 2
University of Liverpool 2
Bilkent University 2
University of Quebec in Outaouais 2
University of Houston 2
Victoria University Melbourne 2
Missouri University of Science and Technology 2
Kent State University 2
Hong Kong Baptist University 2
Istituto Di Calcolo E Reti Ad Alte Prestazioni, Rende 2
Vishwakarma Institute of Technology 2
University of California, San Diego 2
Microsoft Research Asia 2
Tel Aviv University 2
Institute of High Performance Computing, Singapore 2
Huazhong University of Science and Technology 2
University of Glasgow 2
Nanjing University of Science and Technology 2
Johns Hopkins University 2
University of Minnesota System 2
University of Massachusetts Dartmouth 2
South China University of Technology 2
The University of North Carolina at Charlotte 2
Drexel University 2
University of Maryland, Baltimore County 2
Thapar University 2
Indian Institute of Technology Patna 2
Research Organization of Information and Systems National Institute of Informatics 2
Max Planck Institute for Ornithology 2
Federal University of Vicosa 2
University of Quebec in Montreal 2
University of Electronic Science and Technology of China 2
University of Athens 2
University of Kansas Lawrence 2
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 2
Swiss Federal Institute of Technology, Zurich 2
Montclair State University 2
University of Ottawa, Canada 2
Nokia Corporation 2
Polytechnic Institute of Turin 2
University of Nebraska at Omaha 2
Indiana University 2
University of Tokyo 2
The University of Hong Kong 2
University of California, Los Angeles 2
University of Virginia 2
Harvard University 2
Institute for Systems and Computer Engineering of Porto 2
Beijing University of Technology 3
The Chinese University of Hong Kong, Shenzhen 3
International Institute of Information Technology Hyderabad 3
University of Hildesheim 3
INSA Lyon 3
Nokia Bell Labs 3
Shandong University of Science and Technology 3
Hefei University of Technology 3
Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India 3
Southern Illinois University at Carbondale 3
The University of British Columbia 3
Institute of Automation Chinese Academy of Sciences 3
Xerox Corporation 3
University of Massachusetts Boston 3
University of Kentucky 3
Bar-Ilan University 3
Binghamton University State University of New York 3
Seoul National University 3
University of Alberta 3
Facebook, Inc. 3
University of Texas M. D. Anderson Cancer Center 3
University of California, Berkeley 3
Japan Advanced Institute of Science and Technology 3
Indian Institute of Technology, Kharagpur 3
Academia Sinica Taiwan 3
University of Queensland 3
University of California, Davis 3
Qatar Computing Research institute 3
Italian National Research Council 3
Imperial College London 3
Universite Francois-Rabelais Tours 3
Lawrence Livermore National Laboratory 3
Los Alamos National Laboratory 3
University of Pennsylvania 3
University of East Anglia 3
Shanxi University 3
University of Sheffield 3
Boston University 4
University Michigan Ann Arbor 4
University of Roma La Sapienza 4
North Carolina State University 4
Universitat Politecnica de Catalunya 4
Dalhousie University 4
Yahoo Research Barcelona 4
IBM Research 4
Johannes Gutenberg University Mainz 4
Athens University of Economics and Business 4
Monash University 4
University of Antwerp 4
Microsoft Corporation 4
Brookhaven National Laboratory 4
Northeastern University 4
University of Washington, Seattle 4
AT&T Inc. 4
University of Pisa 4
Emory University 4
The University of Western Ontario 4
Beihang University 4
Max Planck Institute for Informatics 4
Institute for Infocomm Research, A-Star, Singapore 4
Shanghai Jiaotong University 4
Universite Paris Sorbonne - Paris IV 4
Rice University 5
The University of North Carolina at Chapel Hill 5
Rutgers, The State University of New Jersey 5
University of Texas at San Antonio 5
The University of Sydney 5
Case Western Reserve University 5
George Mason University 5
University of Southern California 5
University of Southern California, Information Sciences Institute 5
Singapore Management University 5
Sandia National Laboratories, New Mexico 5
New Jersey Institute of Technology 5
Brown University 5
Chinese Academy of Sciences 5
University of Massachusetts Amherst 6
Dalian University of Technology 6
AT&T Laboratories Florham Park 6
Ludwig Maximilian University of Munich 6
Kyoto University 6
National University of Singapore 6
Yahoo Inc. 6
University of Turin 6
University of Minnesota Twin Cities 6
University of California, Santa Barbara 6
University of California, Irvine 6
Nippon Telegraph and Telephone Corporation 6
University of Wisconsin Madison 7
University of Florida 7
Wuhan University 7
University of Maryland 7
University of California, Riverside 7
Renmin University of China 7
Massachusetts Institute of Technology 7
Ben-Gurion University of the Negev 7
Delft University of Technology 8
Google Inc. 8
Federal University of Minas Gerais 8
Xi'an Jiaotong University 8
Pennsylvania State University 8
Rutgers University-Newark Campus 8
University of Texas at Austin 8
Yahoo Research Labs 9
Aalto University 9
University of Sao Paulo 9
Nanyang Technological University 9
Ohio State University 9
Purdue University 9
Microsoft Research 9
Oregon State University 9
IBM Thomas J. Watson Research Center 10
National Taiwan University 10
Florida International University 10
University of Melbourne 10
Stony Brook University 10
Stanford University 10
Rensselaer Polytechnic Institute 11
Sharif University of Technology 11
Georgia Institute of Technology 12
Peking University 13
University of Science and Technology of China 13
Nanjing University 13
Hong Kong University of Science and Technology 14
University of Illinois at Chicago 14
University of Texas at Arlington 15
University of Technology Sydney 16
Cornell University 16
University of Calabria 16
Northwestern Polytechnical University China 16
Simon Fraser University 17
Virginia Tech 18
NEC Laboratories America, Inc. 19
University of Illinois at Urbana-Champaign 19
Chinese University of Hong Kong 24
Tsinghua University 38
Carnegie Mellon University 44
Arizona State University 50
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