ACM DL

ACM Transactions on

Knowledge Discovery from Data (TKDD)

Menu
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)

NEWS

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.

READ MORE
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.

Local Spectral Clustering for Overlapping Community Detection

Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is crucial to shift our attention from macroscopic structure to microscopic structure in large networks. A growing body of work has been adopting local expansion methods in order to identify the community members from a few exemplary seed members. In this paper, we propose a novel approach for finding overlapping communities called LEMON (Local Expansion via Minimum One Norm). The algorithm finds the community by seeking a sparse vector in the span of the local spectra such that the seeds are in its support. We show that LEMON can achieve the highest detection accuracy among state-of-the-art proposals. The running time depends on the size of the community rather than that of the entire graph. The algorithm is easy to implement, and is highly parallelizable. We further provide theoretical analysis on the local spectral properties, bounding the measure of tightness of extracted community in terms of the eigenvalues of graph Laplacian. Moreover, given that networks are not all similar in nature, a comprehensive analysis on how the local expansion approach is suited for uncovering communities in different networks is still lacking. We thoroughly evaluate our approach using both synthetic and real-world datasets across different domains, and analyze the empirical variations when applying our method to inherently different networks in practice. In addition, the heuristics on how the seed set quality and quantity would affect the performance are provided.

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.

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.

Mining Overlapping Communities and Inner Role Assignments through Bayesian Mixed-Membership Models of Networks with Context-Dependent Interactions

The seamless integration of community discovery and role assignment has been recently proposed as an unsupervised approach to the exploratory analysis of networks, aimed to unveil the participation of nodes in multiple overlapping communities along with the roles played therein. One limitation of these prototypical research efforts is that the formation of ties is not truly realistic, since it does not account for a fundamental aspect of link establishment in real-world networks, i.e., the explicative reasons that cause interactions among nodes. Such reasons can be abstractedly interpreted as generic requirements of nodes that are met by other nodes and essentially pertain both to the nodes themselves and to their different interaction contexts (i.e., the respective communities and roles). In this manuscript, we present two new model-based machine-learning approaches, wherein community discovery and role assignment are tightly integrated and simultaneously performed through approximate posterior inference in Bayesian mixed-membership models of directed networks. The two proposed models account for the explicative reasons governing link establishment in terms of node-specific and contextual latent interaction factors. The former are inherently characteristic of nodes, while the latter are characterizations of nodes in the context of the individual communities and roles. The generative process of the devised models assigns nodes to communities with respective roles and connects them through directed links, which are probabilistically governed by their node-specific and contextual interaction factors. The two proposed models differ in the impact of the contextual interaction factors on link generation. We develop MCMC algorithms implementing approximate posterior inference and parameter estimation within our models. Finally, we demonstrate their superiority in community compactness and link prediction via an intensive comparative experimentation on real-world and synthetic networks.

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%.

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.

Data Stream Evolution Diagnosis using Recursive Wavelet Density Estimators

Data streams are a new class of data that is becoming pervasively important in a wide range of applications, ranging from sensor networks, environmental monitoring to finance. In this paper, we propose a novel framework for online characterisation and diagnosing the evolution of multidimensional streaming data which incorporates Recursive Wavelet Density Estimators into the context of Velocity Density Estimation. In the proposed framework changes in streaming data are characterised by the use of local and global evolution coefficients. In addition, we propose for the analysis of changes in the correlation structure of the data a recursive implementation of Pearson correlation coefficient using exponential discounting. Two visualisation tools namely, temporal and spatial velocity profiles, are extended in the context of our framework. Three are the main advantages of the proposed method over previous approaches: 1) the memory storage required is minimal and independent of any window size; 2) it has a significantly lower computational complexity; and 3) it makes possible the fast diagnosis of data evolution at all dimensions and at relevant combinations of dimensions with only one pass of the data. With the help of three examples, we show the frameworks relevance in a change detection context and its potential capability for real world applications.

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.

Bibliometrics

Publication Years 2007-2017
Publication Count 299
Citation Count 2787
Available for Download 299
Downloads (6 weeks) 3210
Downloads (12 Months) 29187
Downloads (cumulative) 208238
Average downloads per article 696
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)
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 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
Neil Shah 2
Wei Wang 2
Eli Upfal 2
Kristina Lerman 2
Pinghui Wang 2
Hao Huang 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
Jin Huang 2
Indrajit Bhattacharya 2
Panagis Magdalinos 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
Jiawei Han 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
Hari Sundaram 2
Mohamed Bouguessa 2
Spiros Papadimitriou 2
Joydeep Ghosh 2
Wei Fan 2
Dino Pedreschi 2
Charu Aggarwal 2
Jon Kleinberg 2
Alceu Costa 1
Yihan Wang 1
Qinbao Song 1
Michele Coscia 1
Yi Wang 1
Charles Elkan 1
João Gama 1
Jaideep Srivastava 1
Carlos Guestrin 1
Naonori Ueda 1
Qi Lou 1
Wei Fan 1
Tomoharu Iwata 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
Qiang Qu 1
Shuhui Wang 1
Can Chen 1
Erik Saule 1
Tao Ku 1
Abhisek Kundu 1
Seunghee Bae 1
Yunhong Hu 1
Pedro Vaz De Melo 1
Jeffrey Chan 1
Michael Houle 1
Dimitrios Gunopulos 1
Daxin Jiang 1
Muna Al-Razgan 1
Mohsen Bayati 1
Jingchao Ni 1
Peilin Zhao 1
Lei Zhang 1
Raymond Wong 1
Ada Fu 1
Li Zheng 1
Noman Mohammed 1
Jaideep Vaidya 1
Collin Stultz 1
Boleslaw Szymanski 1
Maguelonne Teisseire 1
Chao Liu 1
Paolo Boldi 1
Tharam Dillon 1
Yixin Chen 1
Xuanhong Dang 1
Shumo Chu 1
Lini Thomas 1
Sachindra Joshi 1
Luigi Pontieri 1
Francesco Bonchi 1
Kasim Candan 1
Sunil Vadera 1
Thomas Porta 1
Bingrong Lin 1
S Upham 1
Hongzhi Yin 1
Jeffrey Erman 1
Ming Li 1
Ashton Anderson 1
Sendhil Mullainathan 1
Qing He 1
Kai Zhang 1
Yue Wu 1
Christos Anagnostopoulos 1
Dora Erdős 1
Joydeep Ghosh 1
Kaiyuan Zhang 1
Fosca Giannotti 1
James Cheng 1
U Kang 1
Carlos Ordonez 1
Peter Christen 1
Daniel Dunlavy 1
David Dominguez-Sal 1
Christos Doulkeridis 1
Steven Skiena 1
Hiroshi Motoda 1
Danai Koutra 1
Chris Volinsky 1
Andreas Krause 1
Hsiangfu Yu 1
Aditya Parameswaran 1
Binbin Lin 1
Johannes Gehrke 1
Christo Wilson 1
Zhishan Guo 1
Yunsing Koh 1
Silei Xu 1
Leonid Hrebien 1
Pei Yang 1
Li Li 1
Denian Yang 1
Ben Zhao 1
Bo Liu 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
Ana Maguitman 1
Filippo Menczer 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
Wei Wei 1
Guoqing Chen 1
Xiaojun Chang* 1
Lina Yao 1
Xin Jin 1
Tao Yang 1
Polina Rozenshtein 1
Martin Rosvall 1
Duygu Ucar 1
Mustafa Bilgic 1
Zhao Kang 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
Jérémy Besson 1
Raghu Ramakrishnan 1
Rong Ge 1
Byronju Gao 1
Li Tu 1
Saharon Rosset 1
Claudia Perlich 1
Tuannhon Dang 1
Satyanarayana Valluri 1
Ashish Verma 1
Seekiong Ng 1
Ramana Kompella 1
Chengkai Li 1
Salvatore Ruggieri 1
Hong Xie 1
Vasileios Kandylas 1
Jing Zhang 1
Rodrigo Alves 1
Juhua Hu 1
Yu Jin 1
Yunfei Lu 1
Wenwu Zhu 1
Ahmed El-Mahdy 1
Jeffreyxu Yu 1
Soroush Vosoughi 1
Giulio Rossetti 1
Veerabhadran Baladandayuthapani 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
Maryam Tahani 1
Hamid Rabiee 1
Yubao Wu 1
Shifeng Weng 1
Ying Wei 1
Junming Shao 1
Yllka Velaj 1
Xiaojun Chang 1
Lars Schmidt-Thieme 1
Jun Yan 1
James Bezdek 1
Marimuthu Palaniswami 1
Jayavardhana Gubbi 1
Michael Lyu 1
Dityan Yeung 1
Evangelos Papalexakis 1
Nicholas Sidiropoulos 1
George Karypis 1
Jilei Tian 1
Davoud Moulavi 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
Masaru Kitsuregawa 1
Xiang Zhang 1
Jenwei Huang 1
James Bailey 1
Jianping Zhang 1
Manas Somaiya 1
Graham Cormode 1
Bin Li 1
Fernando Kuipers 1
Dick Epema 1
Linpeng Tang 1
Min Wang 1
Marc Maier 1
Lionel Ni 1
MingXi Wu 1
Benjamin Fung 1
Ye Chen 1
John Canny 1
Dominique Laurent 1
Yeowwei Choong 1
Meghana Deodhar 1
Luca Becchetti 1
Keli Xiao 1
Hans Kriegel 1
Gunjan Gupta 1
Ling Feng 1
Diana Inkpen 1
Kuan Zhang 1
Ying Cui 1
Vetle Torvik 1
Bo Long 1
Edoardo Serra 1
Luigi Moccia 1
Nesreen Ahmed 1
Min Wang 1
Ali Pınar 1
Claudio Schifanella 1
Shuiwang Ji 1
Michail Vlachos 1
Ling Chen 1
Yang Liu 1
Chunxiao Xing 1
Dechuan Zhan 1
Ruggero Pensa 1
Bryan Hooi 1
Tetsuji Ogawa 1
Yuto Yamaguchi 1
Shaoxu Song 1
Saurabh Paul 1
Jose Hern´ndez-Orallo 1
Rainer Gemulla 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
Maya Bercovitch 1
Maryam Ramezani 1
Shebuti Rayana 1
Michalis Faloutsos 1
Hongxia Yang 1
Haoda Fu 1
Dawei Zhou 1
Jingrui He 1
Liming Chen 1
Jure Leskovec 1
Stefan Kramer 1
Huaimin Wang 1
Qiang You 1
Luke McDowell 1
Miao Tian 1
Naren Ramakrishnan 1
Qi Tian 1
Jennifer Neary 1
Minoru Kanehisa 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
Huilei He 1
Hua Wang 1
Guannan Liu 1
Liang Wang 1
Kimon Fountoulakis 1
Shanshan Feng 1
Fei Zou 1
Virgílio Almeida 1
Laiwan Chan 1
Nitin Agarwal 1
S Muthukrishnan 1
Christos Faloutsos 1
Anthony Tung 1
Kunta Chuang 1
Amin Saberi 1
Adelelu Jia 1
Alexandru Iosup 1
Aniket Chakrabarti 1
Reza Zafarani 1
Saurabh Kataria 1
Matthew Rattigan 1
Geoffrey Barbier 1
Limin Yao 1
Cheukkwong Lee 1
Olvi Mangasarian 1
Chris Clifton 1
Mohammed Zaki 1
Jennifer Dy 1
Loïc Cerf 1
Henry Tan 1
Shaojun Wang 1
Gianluigi Greco 1
Francesco Gullo 1
Guimei Liu 1
Min Ding 1
Jennifer Neville 1
Gensheng Zhang 1
Yiming Yang 1
Vassilios Vassiliadis 1
Kaiming Ting 1
Kijung Shin 1
Lorenzo De Stefani 1
Alessandro Epasto 1
Caetanotraina Jr 1
Oualid Boutemine 1
Ayan Acharya 1
Sreangsu Acharyya 1
Arnold Boedihardjo 1
Changtien Lu 1
Zhiqiang Xu 1
Christophe Giraud-Carrier 1
Aditya Menon 1
Zhongfei Zhang 1
Matthew Rowe 1
Edward Chang 1
Kazumi Saito 1
Chengxiang Zhai 1
Dong Xin 1
Christian Böhm 1
Dafna Shahaf 1
Stephen Fienberg 1
Raviv Raich 1
Bilson Campana 1
Vibhor Rastogi 1
Deng Cai 1
Sigal Sina 1
Sarit Kraus 1
Chris Ding 1
Lior Rokach 1
Dityan Yeung 1
Xiaolin Wang 1
Tingting Gao 1
Leman Akoglu 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
Lorenzo Severini 1
Ou Wu 1
Lei Ma 1
Xing Yong 1
T Murali 1
Kiyoko Aoki-Kinoshita 1
Ravi Janardan 1
Sudhir Kumar 1
Ming Zhang 1
Shlomi Dolev 1
Fang Wang 1
Haixun Wang 1
Zhirui Hu 1
Dheeraj Kumar 1
Yao Wu 1
Dandan Qiao 1
Tiancheng Lou 1
Guna Seetharaman 1
Giacomo Berardi 1
Kathleen Carley 1
Feng Chen 1
Xiaotong Zhang 1
Han Liu 1
Eugenia Kontopoulou 1
Xiaodan Song 1
Wei Wang 1
Yasuhiro Fujiwara 1
ChienWei Chen 1
Gianlorenzo D'Angelo 1
Yu Zheng 1
Saurav Sahay 1
Tanmoy Chakraborty 1
David Leake 1
Chenguang Wang 1
Zhoujun Li 1
Neilzhenqiang Gong 1
Yi Chang 1
Qiang Wei 1
Xiaowen Ding 1
Jörg Sander 1
Siyuan Liu 1
Ahmet Sarıyüce 1
Changtien Lu 1
Sen Wang 1
Bill Howe 1
Nicholasjing Yuan 1
Yu Yang 1
Chong Peng 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
Jude Shavlik 1
Sibel Adalı 1
Xiaohui Lu 1
Yin Zhang 1
Qian Sun 1
Zhongfei Zhang 1
Yunxin Zhao 1
Domenico Saccà 1
Francesco Lupia 1
Nima Mirbakhsh 1
Antti Ukkonen 1
Xindong Wu 1
Zheng Wang 1
Johannes Schneider 1
Bin Cui 1
Juanzi Li 1
Patrick Haffner 1
Zhili Zhang 1
Qingyan Yang 1
Lei Xie 1
Hyunah Song 1
Haifeng Chen 1
Xiang Zhang 1
Hao Ye 1
Nenghai Yu 1
Peter Triantafillou 1
Christos Boutsidis 1
Bingsheng Wang 1
Scott Burton 1
Hui Ke 1
Tamara Kolda 1
Jie Wang 1
Karthik Subbian 1
João Duarte 1
Yulan He 1
Maria Halkidi 1
David Gleich 1
Steven Hoi 1
Lei Zou 1
Luming Zhang 1
Jian Wang 1
Manos Papagelis 1
Ruud Van De Bovenkamp 1
Clyde Giles 1
Wei Peng 1
David Jensen 1
Tengfei Bao 1
Brook Wu 1
Glenn Fung 1
Zeeshan Syed 1
Peer Kröger 1
Céline Robardet 1
Jean Boulicaut 1
Pradeep Tamma 1
Zengjian Hu 1
Boaz Ben-Moshe 1
James Cheng 1
Shachar Kaufman 1
Ori Stitelman 1
Leland Wilkinson 1
Kamalakar Karlapalem 1
Neil Smalheiser 1
Dale Schuurmans 1
Dimitrios Mavroeidis 1
José Balcázar 1
Hockhee Ang 1
Weekeong Ng 1
Mengling Feng 1
Xiao Jiang 1
Franco Turini 1
Comandur Seshadhri 1
Luan Tang 1
Lyle Ungar 1
Quanquan Gu 1
Xintao Wu 1
Nick Duffield 1
Chun Li 1
Jianyong Wang 1
Feitony Liu 1
Tao Mei 1
Essam Algizawy 1
Galileo Namata 1
John Frenzel MD 1
Hua Duan 1
Yandong Liu 1
Qiaozhu Mei 1
Joshua Vogelstein 1
Takeshi Yamada 1
Suresh Iyengar 1
Jiawei Han 1
Ashwin Machanavajjhala 1
Erheng Zhong 1
Wei Fan 1
Ali Hemmatyar 1
Meng Jiang 1
Beilun Wang 1
Chihya Shen 1
Zhitao Wang 1
Jingrui He 1
Yi Zhen 1
Deb Roy 1
Sanjay Chawla 1
Jinpeng Wang 1
Arnau Prat-Pérez 1
Josep Larriba-Pey 1
Risa Myers 1
Qingtian Zeng 1
Brian Gallagher 1
John Hutchins 1
Taneli Mielikäinen 1
Ji Liu 1
Manuel Gomez-Rodriguez 1
Sethuraman Panchanathan 1
Abdullah Mueen 1
Yizhou Sun 1
Xiaofei He 1
Muthuramakrishnan Venkitasubramaniam 1
Zhi Yang 1
Moshe Kam 1
Jieping Ye 1
Licong Cui 1
Xiaofeng Zhu 1
Victor Lee 1
Robert Kleinberg 1
Yafei Dai 1
Pierluigi Crescenzi 1
Zijun Yao 1
Weiming Hu 1
Maoying Qiao 1
Wei Bian 1
Ying Jin 1
Carlos Lorenzetti 1
Dan Roth 1
Hiroshi Mamitsuka 1
Thomas Reichherzer 1
Ephraim Korach 1
Jeffrey Ullman 1
Wenyuan Zhu 1
Kai Zheng 1
Zhongyuan Wang 1
Allon Percus 1
Xunhua Guo 1
Ümit Çatalyürek 1
Xutong Liu 1
Yencheng Lu 1
Xue Li 1
Guodong Long 1
Sitaram Asur 1
Jerry Kiernan 1
Jie Cheng 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
Murat Kantarcıoğlu 1
John Guttag 1
Marc Plantevit 1
Fan Guo 1
Alin Dobra 1
Binay Bhattacharya 1
Bin Zhou 1
Anushka Anand 1
Jinlin Chen 1
Yicheng Tu 1
Shantanu Godbole 1
Dan Simovici 1
Hao Wang 1
Siddharth Gopal 1
Madhav Jha 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
Zhen Guo 1
Yashu Liu 1
Waynexin Zhao 1
Faming Lu 1
Andrew Mehler 1
Stephen North 1
Seungil Huh 1
Chojui Hsieh 1
Zheng Wang 1
Thanawin Rakthanmanon 1
Jesin Zakaria 1
Chihjen Lin 1
Kedar Bellare 1
Brandon Norick 1
Ming Ji 1
Yuval Elovici 1
Sri Ravana 1
Shiqiang Yang 1
Zoran Obradović 1
Wangchien Lee 1
Ming Lin 1
Changshui Zhang 1
Qinli Yang 1
Josif Grabocka 1
Nicolas Schilling 1
Xiang Li 1
David Aha 1
Richard Xu 1
Sougata Mukherjea 1
Ashwin Ram 1
Sriram Srinivasan 1
Niloy Ganguly 1
Animesh Mukherjee 1
Sanjukta Bhowmick 1
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
Maha Alabduljalil 1
Daniel Halperin 1
Jian Cao 1
Jie Wang 1
Shiyou Qian 1
Dengyong Zhou 1
Qiang Cheng 1
Ming Zhang 1
Biru Dai 1
Divesh Srivastava 1
Zhenjie Zhang 1
Hungleng Chen 1
Liang Hong 1
Hunghsuan Chen 1
Venu Satuluri 1
Rose Yu 1
Yao Zhang 1
Aisling Kelliher 1
Paul Castro 1
Lian Duan 1
Bruno Ribeiro 1
Siyuan Liu 1
Anon Plangprasopchok 1
Shengrui Wang 1
Patrick Hung 1
Ganesh Ramesh 1
A Patterson 1
Manolis Kellis 1
Carlos Castillo 1
Amit Dhurandhar 1
Beechung Chen 1
Fedja Hadzic 1
Elizabeth Chang 1
Li Wan 1
Weekeong Ng 1
Sethuraman Panchanathan 1
Michael Mampaey 1
Aminul Islam 1
Yu Lei 1
Tianbing Xu 1
Sanmay Das 1
Haojun Zhang 1
Limsoon Wong 1
Maria Sapino 1
Shipeng Yu 1
Zhiting Hu 1
Pedro Melo 1
Yuan Jiang 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
Amazon.com, 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

ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on KDD 2016 and Regular Papers
Archive


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

2016
Volume 11 Issue 2, December 2016
Volume 11 Issue 1, August 2016
Volume 10 Issue 4, July 2016 Special Issue on SIGKDD 2014, Special Issue on BIGCHAT and Regular Papers
Volume 10 Issue 3, February 2016

2015
Volume 10 Issue 2, October 2015
Volume 10 Issue 1, July 2015
Volume 9 Issue 4, June 2015
Volume 9 Issue 3, April 2015 TKDD Special Issue (SIGKDD'13)

2014
Volume 9 Issue 2, November 2014
Volume 9 Issue 1, October 2014
Volume 8 Issue 4, October 2014
Volume 8 Issue 3, June 2014
Volume 8 Issue 2, June 2014
Volume 8 Issue 1, February 2014 Casin special issue

2013
Volume 7 Issue 4, November 2013
Volume 7 Issue 3, September 2013 Special Issue on ACM SIGKDD 2012
Volume 7 Issue 2, July 2013
Volume 7 Issue 1, March 2013

2012
Volume 6 Issue 4, December 2012 Special Issue on the Best of SIGKDD 2011
Volume 6 Issue 3, October 2012
Volume 6 Issue 2, July 2012
Volume 6 Issue 1, March 2012
Volume 5 Issue 4, February 2012

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

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

2009
Volume 3 Issue 4, November 2009
Volume 3 Issue 3, July 2009
Volume 3 Issue 2, April 2009
Volume 3 Issue 1, March 2009
Volume 2 Issue 4, January 2009

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

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

Search TKDD
enter search term and/or author name