ACM Transactions on

Knowledge Discovery from Data (TKDD)

Latest Articles

Distributed Algorithms for Computing Very Large Thresholded Covariance Matrices

Computation of covariance matrices from observed data is an important problem, as such matrices are used in applications such as principal component... (more)

World Knowledge as Indirect Supervision for Document Clustering

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires... (more)

Permanence and Community Structure in Complex Networks

The goal of community detection algorithms is to identify densely connected units within large networks. An implicit assumption is that all the... (more)

Partitioning Networks with Node Attributes by Compressing Information Flow

Real-world networks are often organized as modules or communities of similar nodes that serve as functional units. These networks are also rich in... (more)

Scalable and Accurate Online Feature Selection for Big Data

Feature selection is important in many big data applications. Two critical challenges closely associate with big data. First, in many big data... (more)

Structural Analysis of User Choices for Mobile App Recommendation

Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in... (more)

Assignment Problems of Different-Sized Inputs in MapReduce

A MapReduce algorithm can be described by a mapping schema, which assigns inputs to a set of reducers, such that for each required output there exists... (more)

Unsupervised Head--Modifier Detection in Search Queries

Interpreting the user intent in search queries is a key task in query understanding. Query intent classification has been widely studied. In this... (more)

Lifecycle Modeling for Buzz Temporal Pattern Discovery

In social media analysis, one critical task is detecting a burst of topics or buzz, which is reflected by extremely frequent mentions of certain... (more)

A Novel Bipartite Graph Based Competitiveness Degree Analysis from Query Logs

Competitiveness degree analysis is a focal point of business strategy and competitive intelligence, aimed to help managers closely monitor to what... (more)

Comparing Clustering with Pairwise and Relative Constraints

Clustering can be improved with the help of side information about the similarity relationships among instances. Such information has been commonly... (more)

Mining for Topics to Suggest Knowledge Model Extensions

Electronic concept maps, interlinked with other concept maps and multimedia resources, can provide rich knowledge models to capture and share human... (more)


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About TKDD 

The 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
Combining Structured Node Content and Topology Information for Networked Graph Clustering

In this paper, we formulate a new networked graph clustering task where the network contains a set of inter-connected (or networked) super-nodes, each of which is a single-attribute graph. The new super-node representation is applicable for many real-world applications, such as a citation network where each node denotes a paper whose content can be described as a graph and citation relationships between all papers form a super-graph. Networked graph clustering is to find similar node groups, each of which contains nodes with similar content and structure information. The main challenge is to properly calculate the similarity between super-nodes for clustering. To solve the problem, we propose to characterize node similarity by integrating the structure and content information of each super-node. To measure node content similarity, we use cosine distance by considering the overlapped attributes between two super-nodes. To measure structure similarity, we propose an attributed random walk kernel to calculate similarity between super-nodes. Detailed node content analysis is also included to build relationships between super-nodes with shared internal structure information, so the structure similarity can be calculated in a precise way. By integrating the structure similarity and content similarity as one matrix, the spectral clustering is used to achieve networked graph clustering.

Recommendations Based on Comprehensively Exploiting the Latent Factors Hidden in Items' Ratings and Content

In this study, we propose a probabilistic approach that we denote as latent random walk (LRW) based on the combination of an integrated latent topic model and random walk (RW) with the restart method, which can be used to rank items according to expected user preferences by detecting both their explicit and implicit correlative information, in order to recommend top-ranked items to potentially interested users. As presented in this paper, the goal of this work is to comprehensively discover latent factors hidden in items' ratings and content in order to alleviate the data sparsity problem and to improve the performance of recommender systems. The proposed topic model provides a generative probabilistic framework that discovers users' implicit preferences and items' latent features simultaneously by exploiting both ratings and item content information. On the basis of this probabilistic framework, RW can predict a user's preference for unrated items by discovering global latent relations. In order to show the efficiency of the proposed approach, we test LRW and other state-of-the-art methods on two real-world datasets, namely, the Yahoo! and CAMRa2011 movie datasets. The experiments indicate that our approach outperforms all comparative methods and, in addition, that it is less sensitive to the data sparsity problem, thus demonstrating the robustness of LRW for recommendation tasks.

Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models

Modern visual analytic tools promote human-in-the-loop analysis but are limited in their ability to direct the user toward interesting and promising directions of study. This problem is especially acute when the analysis task is exploratory in nature, e.g., the discovery of potentially coordinated relationships in massive text datasets. Such tasks are very common in domains like intelligence analysis and security forensics where the goal is to uncover surprising coalitions bridging multiple types of relations. We introduce new maximum entropy models to discover surprising chains of relationships leveraging count data about entity occurrences in documents. These models are embedded in a visual analytic system called BiSet that treats relationship bundles as first class objects and directs the user toward promising lines of inquiry. We demonstrate how user input can judiciously direct analysis toward valid conclusions whereas a purely algorithmic approach could be led astray. Experimental results on both synthetic and real datasets from the intelligence community are presented.

Robust Graph Regularized Nonnegative Matrix Factorization for Clustering

Matrix factorization is often used for data representation in many data mining and machine learning areas. In particular, for a data set without any negative entries, nonnegative matrix factorization is often used to find a low-rank approximation by the product of two nonnegative matrices. With reduced dimensions, these matrices can be effectively used for many applications such as clustering. The existing methods of nonnegative matrix factorization are often afflicted with their sensitivity to outliers and noise in the data. To mitigate this drawback, in this paper we consider a robust formulation that effectively captures noise and outliers in the approximation while incorporating essential nonlinear structures. A set of comprehensive empirical evaluations in clustering applications demonstrates that the proposed method has strong robustness to gross errors and superior performance to current state-of-the-art methods.

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 management, and industrial optimization. Recently, invariant network has shown to be powerful in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1) fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a high-percentage of vanishing correlations; 3) temporal patterns of vanishing correlations are not exploited for robust detection; 4) prior knowledges on anomalous nodes are not exploited for (semi-)supervised detection. To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system. Moreover, when the prior knowledges on the anomalous status of some nodes are available at certain time points, our approach is able to leverage them to further enhance the anomaly inference accuracy. When the prior knowledges are noisy, our approach also automatically learns reliable information and reduces impacts from noises. By performing extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets, we demonstrate the effectiveness of our approach.

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 decisions via an algorithm. Motivated by this development, an emerging line of work has begun to consider whether we can characterize and predict the kinds of decisions where people are likely to make errors. To investigate what a general framework for human error prediction might look like, we focus on a model system with a rich history in the behavioral sciences: the decisions made by chess players as they select moves in a game. We carry out our analysis at a large scale, employing datasets with several million recorded games, and using chess tablebases to acquire a form of ground truth for a subset of chess positions that have been completely solved by computers but remain challenging for even the best players in the world. We organize our analysis around three categories of features that we argue are present in most settings where the analysis of human error is applicable: the skill of the decision-maker, the time available to make the decision, and the inherent difficulty of the decision. We identify rich structure in all three of these categories of features, and find strong evidence that in our domain, features describing the inherent difficulty of an instance are significantly more powerful than features based on skill or time.

A Randomized Rounding Algorithm for Sparse PCA

We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem. Our approach first solves an l1-penalized version of the NP-hard sparse PCA optimization problem and then uses a randomized rounding strategy to sparsify the resulting dense solution. Our main theoretical result guarantees an additive error approximation and provides a tradeoff between sparsity and accuracy. Our experimental evaluation indicates that our approach is competitive in practice, even compared to state-of-the-art toolboxes such as Spasm.

Partitioned Similarity Search with Cache-Conscious Data Traversal

All pairs similarity search (APSS) is used in many web search and data mining applications. Previous work has used comparison filtering, inverted indexing, and parallel accumulation of partial intermediate results to expedite its execution. However, shuffling intermediate results can incur significant communication overhead as data scales up. This paper studies a scalable two-step approach called Partition-based Similarity Search (PSS). The first stage is to partition the data and group potentially similar vectors. The second stage is to run a set of tasks where each task compares a partition of vectors with other candidate partitions. Because of data sparsity, accessing feature vectors in memory during runtime partition comparison incurs significant overhead due to the presence of memory hierarchy. This paper also optimizes data traversal with a cache-conscious layout to reduce the execution time through size-controlled data splitting and vector coalescing, and provides an analysis to guide the optimal choice for the parameter setting. The evaluation results show that the proposed approach leads to an early elimination of unnecessary I/O and data communication while sustaining parallel efficiency with one order of magnitude of performance improvement and it can also be integrated with LSH for approximated APSS.

Learning Multiple Diagnosis Codes for ICU Patients with Local Disease Correlation Mining

In the era of big data, a mechanism that can automatically annotate disease codes to patients' records in the medical information system is in demand. The purpose of this work is to propose a framework that automatically annotates disease labels of multi-source patient data in ICU. We extract features from two main sources, medical charts and notes. The Bag-of-Words (BoW) model is used to encode the features. Different from most of the existing multi-label learning algorithms that globally consider correlations among diseases, our model learns disease correlation locally in the patient data. To achieve this, we derive a local disease correlation representation to enrich the discriminant power of each patient data. This representation is embedded into a unified multi-label learning framework. We develop an alternating algorithm to optimize the objective function iteratively. Extensive experiments have been conducted on a real-world Intensive Care Units (ICU) database. We have compared our algorithm with representative multi-label learning algorithms. Evaluation results have shown that our proposed method has state-of-the-art performance in multiple diagnostic codes annotation for ICU patients. This study suggests that the problems in the automated diagnosis code annotation can be reliably addressed by using a multi-label learning model with disease correlation exploiting. The findings of this study will redound to the benefits of health care and management in ICU considering that the automated diagnosis code annotation can significantly improve health care quality and management for both patients and caregivers.

Mining Redescriptions with Siren

In many areas of science, scientists need to find distinct common characterizations of the same objects and, vice versa, identify sets of objects that admit multiple shared descriptions. For example, in biology, an important task is to identify the bioclimatic constraints that allow some species to survive, that is, to describe geographical regions in terms of both the fauna that inhabits them and their bioclimatic conditions. In data analysis, the task of automatically generating such alternative characterizations is called redescription mining. If a domain expert wants to use redescription mining in his research, merely being able to find them is not enough; he must also be able to understand the redescriptions found, adjust them to better match his domain knowledge, test alternative hypotheses with them, and guide the mining process towards results he considers interesting. To facilitate these goals, we introduce Siren, an interactive tool for mining and visualizing redescriptions. Siren allows for efficient, distributed mining of the redescriptions in an anytime fashion, various linked visualizations of the results, interaction with the results either directly or via the visualizations, and guiding the mining algorithm for specific redescriptions. In this paper we explain the features of Siren and why they are useful for redescription mining. We also propose two novel redescription mining algorithms that improve the generalizability of the results compared to the existing results.

Moving Destination Prediction Using Sparse Dataset: A Mobility Gradient Descent Approach

Moving destination prediction offers an important category of location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to destination prediction is to match the given query trajectory with massive recorded trajectories by similarity calculation. Unfortunately, due to privacy concerns, budget constraints and many other factors, in most circumstances, we can only obtain a sparse trajectory dataset. In sparse dataset, the available moving trajectories are far from enough to cover all possible query trajectories, thus the predictability of the matching-based approach will decrease remarkably. Towards destination prediction with sparse dataset, instead of searching similar trajectories over the sparse records, we alternatively examine the changes of distances from sampling locations to final destination on query trajectory. The underlying idea is intuitive: it is directly motivated by travel purpose, people always gets closer to the final destination during the movement. By borrowing the conception of gradient descent in optimization theory, we propose a novel moving destination prediction approach, namely MGDPre. Building upon the mobility gradient descent, MGDPre only investigates the behavior characteristics of query trajectory itself without matching historical trajectories, thus is applicable for sparse dataset. We evaluate our approach based on extensive experiments, using GPS trajectories generated by a sample of taxis over a ten-day period in Shenzhen city, China. The results demonstrate that the effectiveness, efficiency and scalability of our approach outperforms state-of-the-art baseline methods.

Scalable and Efficient Flow-Based Community Detection for Large-Scale Graph Analysis

Community detection is a powerful approach to uncover important structures in large networks. For real networks that often describe the flow of some entity, flow-based community detection methods are particularly important. Infomap is a flow-based community detection algorithm that optimizes the objective function known as the map equation. Third-party benchmarks have found that Infomap is the most effective algorithm for identifying clusters in large graphs. Unfortunately, though Infomap works well, it is an inherently serial algorithm and thus cannot take advantage of multi-core processing in modern computers, limiting its use for analyzing large graphs quickly. In this paper, we propose a novel algorithm to optimize the map equation called RelaxMap. RelaxMap provides two important improvements over Infomap: parallelization, so that the map equation can be optimized over much larger graphs, and prioritization, so that the most important work occurs first, iterations take less time, and the algorithm converges faster. We implement these techniques using OpenMP on shared-memory multicore systems, and evaluate our approach on a variety of graphs from standard graph clustering benchmarks as well as real graph datasets. Our evaluation shows that both techniques are effective: RelaxMap achieves 70% parallel efficiency on 8 cores, and prioritization improves algorithm performance by an additional 20%50%. Additionally, RelaxMap converges in the similar number of iterations and provides solutions of equivalent quality as the serial Infomap implementation.

Graph Manipulations for Fast Centrality Computation


Publication Years 2007-2016
Publication Count 272
Citation Count 2388
Available for Download 272
Downloads (6 weeks) 2569
Downloads (12 Months) 29600
Downloads (cumulative) 186686
Average downloads per article 686
Average citations per article 9
First Name Last Name Award
John Canny ACM Doctoral Dissertation Award (1987)
Carlos A. Castillo ACM Senior Member (2014)
Chris Clifton ACM Senior Member (2006)
Graham R. Cormode ACM Distinguished Member (2013)
Benjamin Fung ACM Senior Member (2013)
John E Hopcroft ACM Karl V. Karlstrom Outstanding Educator Award (2008)
ACM A. M. Turing Award (1986)
Piotr Indyk ACM Paris Kanellakis Theory and Practice Award (2012)
Jon Kleinberg ACM AAAI Allen Newell Award (2014)
ACM Prize in Computing (2008)
Laks Lakshmanan ACM Distinguished Member (2016)
Chih-Jen Lin 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)
Sethuraman Panchanathan ACM Senior Member (2009)
Jian Pei ACM Senior Member (2007)
Domenico Sacca ACM Senior Member (2007)
Jeffrey D Ullman ACM Karl V. Karlstrom Outstanding Educator Award (1997)
Hui Xiong ACM Distinguished Member (2014)
ACM Senior Member (2010)
Qiang Yang ACM Distinguished Member (2011)
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 Distinguished Member (2013)
ACM Senior Member (2011)
Zhi-Hua Zhou ACM Distinguished Member (2013)
ACM Senior Member (2011)

First Name Last Name Paper Counts
Christos Faloutsos 12
Jieping Ye 7
Hui Xiong 5
Jian Pei 5
Tao Li 5
Philip YU 4
Zhihua Zhou 4
Shenghuo Zhu 4
Heng Huang 4
John Lui 4
Feiping Nie 4
Huan Liu 4
Aristides Gionis 4
Christopher Jermaine 4
John Hopcroft 3
Zhiwen Yu 3
Lise Getoor 3
Jure Leskovec 3
Malik Magdon-Ismail 3
Xiaoli Fern 3
Mingsyan Chen 3
Jilles Vreeken 3
Yun Chi 3
Yasushi Sakurai 3
Evimaria Terzi 3
Yihong Gong 3
Lei Tang 3
Bin Guo 3
Hong Cheng 3
Dingding Wang 3
Fabio Fassetti 3
Fabrizio Angiulli 3
Jianhui Chen 2
Yu Zhang 2
Fabrizio Sebastiani 2
Arthur Zimek 2
Yangqiu Song 2
Michalis Vazirgiannis 2
Junzhou Zhao 2
Xiaohong Guan 2
Geoffrey Webb 2
Indrajit Bhattacharya 2
Panagis Magdalinos 2
Qiang Yang 2
Jin Huang 2
Xiao Yu 2
Charles Ling 2
Andrea Esuli 2
Jilei Tian 2
Ping Luo 2
B Prakash 2
Yuru Lin 2
Shinjae Yoo 2
Ruoming Jin 2
Ian Davidson 2
Antonella Guzzo 2
Guofei Jiang 2
Jiawei Han 2
Jiawei Han 2
Antônio Loureiro 2
Hanghang Tong 2
Xiang Zhang 2
Daniel Kifer 2
Yan Liu 2
Laks Lakshmanan 2
Jimeng Sun 2
Don Towsley 2
Rita Chattopadhyay 2
Sucheta Soundarajan 2
Yong Ge 2
Xianchao Zhang 2
Dacheng Tao 2
Wei Ding 2
Belle Tseng 2
Enhong Chen 2
Qi Liu 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
Spiros Papadimitriou 2
Joydeep Ghosh 2
Wei Fan 2
Dino Pedreschi 2
Kristina Lerman 2
Pinghui Wang 2
Hao Huang 2
Hong Qin 2
Yehuda Koren 2
Heikki Mannila 2
Panayiotis Tsaparas 2
Chengqi Zhang 2
Zhu Wang 2
Mohamed Bouguessa 1
Mingxi Wu 1
Ye Chen 1
John Canny 1
Dominique Laurent 1
Yeowwei Choong 1
Benjamin Fung 1
Luca Becchetti 1
Ying Cui 1
Meghana Deodhar 1
Keli Xiao 1
Bo Long 1
Hans Kriegel 1
Gunjan Gupta 1
Ling Feng 1
Hongxia Yang 1
Haoda Fu 1
Dawei Zhou 1
Jingrui He 1
Diana Inkpen 1
Liming Chen 1
Maryam Ramezani 1
Wei Wang 1
Michalis Faloutsos 1
Shebuti Rayana 1
Kuan Zhang 1
Vetle Torvik 1
Luigi Moccia 1
Edoardo Serra 1
Claudio Schifanella 1
Nesreen Ahmed 1
Min Wang 1
Shuiwang Ji 1
Ali Pınar 1
Ling Chen 1
Michail Vlachos 1
Yang Liu 1
Chunxiao Xing 1
Dechuan Zhan 1
Ruggero Pensa 1
Saurabh Paul 1
Jose Hern´ndez-Orallo 1
Eli Upfal 1
Xueying Zhang 1
Rainer Gemulla 1
Guangtao Wang 1
Yiping Ke 1
Evrim Acar 1
Yang Zhou 1
Charu Aggarwal 1
Ben London 1
Joseph Ruiz Md 1
Neil Shah 1
Masahiro Kimura 1
Alexander Ihler 1
Kaiwei Chang 1
Forrest Briggs 1
Gustavo Batista 1
Qiang Zhu 1
Philip Yu 1
Jure Leskovec 1
Jon Kleinberg 1
Maya Bercovitch 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
Irwin King 1
Ling Liu 1
Huilei He 1
Hua Wang 1
Shantanu Sharma 1
Yuanli Pei 1
Teresa Tjahja 1
Wenchih Peng 1
Zekai Gao 1
Sutharshan Rajasegarar 1
Jeffrey Chan 1
Laura Smith 1
Jin Zhang 1
Fei Zou 1
Virgílio Almeida 1
Christos Faloutsos 1
Nitin Agarwal 1
S Muthukrishnan 1
Laiwan Chan 1
Anthony Tung 1
Kunta Chuang 1
Adelelu Jia 1
Alexandru Iosup 1
Aniket Chakrabarti 1
Reza Zafarani 1
Saurabh Kataria 1
Amin Saberi 1
Matthew Rattigan 1
Geoffrey Barbier 1
Limin Yao 1
Olvi Mangasarian 1
Chris Clifton 1
Mohammed Zaki 1
Cheukkwong Lee 1
Jennifer Dy 1
Shaojun Wang 1
Loïc Cerf 1
Henry Tan 1
Yanjun Qi 1
Theodoros Lappas 1
Munmun De Choudhury 1
Wenjie Li 1
Chen Chen 1
Tina Eliassi-Rad 1
Yada Zhu 1
Leman Akoglu 1
Francesco Gullo 1
Gianluigi Greco 1
Guimei Liu 1
Min Ding 1
Jennifer Neville 1
Gensheng Zhang 1
Yiming Yang 1
Vassilios Vassiliadis 1
Kaiming Ting 1
Christophe Giraud-Carrier 1
Ayan Acharya 1
Sreangsu Acharyya 1
Arnold Boedihardjo 1
Changtien Lu 1
Zhiqiang Xu 1
Zhongfei Zhang 1
Matthew Rowe 1
Edward Chang 1
Aditya Menon 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
Bruno Abrahão 1
Xiaolin Wang 1
Tingting Gao 1
Longjie Li 1
Lorenzo Severini 1
Ou Wu 1
Lei Ma 1
Xing Yong 1
T Murali 1
Kiyoko Aoki-Kinoshita 1
Ravi Janardan 1
Sudhir Kumar 1
Tiancheng Lou 1
Guna Seetharaman 1
Giacomo Berardi 1
Xiaotong Zhang 1
Han Liu 1
Kathleen Carley 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
Xiaodan Song 1
Yasuhiro Fujiwara 1
Wei Wang 1
ChienWei Chen 1
Weiyin Loh 1
John Salerno 1
Nitin Kumar 1
Flip Korn 1
Siqi Shen 1
Xinran He 1
Lei Li 1
Ying Wang 1
Ke Wang 1
Jing Zhang 1
Benoît Dumoulin 1
Chris Ding 1
Xiuyao Song 1
John Gums 1
Yin Zhang 1
Zhongfei Zhang 1
Yunxin Zhao 1
Jude Shavlik 1
Beilun Wang 1
Chihya Shen 1
Zhitao Wang 1
Ali Hemmatyar 1
Wei Cheng 1
Meng Jiang 1
Peng Cui 1
Jingrui He 1
Yi Zhen 1
Qian Sun 1
Sibel Adalı 1
Xiaohui Lu 1
Domenico Saccà 1
Nima Mirbakhsh 1
Antti Ukkonen 1
Francesco Lupia 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
Scott Burton 1
Christos Boutsidis 1
Bingsheng Wang 1
Hui Ke 1
Tamara Kolda 1
Jie Wang 1
Karthik Subbian 1
Galileo Namata 1
João Duarte 1
Yulan He 1
John Frenzel MD 1
Joshua Vogelstein 1
Hua Duan 1
Qiaozhu Mei 1
Yandong Liu 1
Takeshi Yamada 1
Suresh Iyengar 1
Jiawei Han 1
Ashwin Machanavajjhala 1
Erheng Zhong 1
Wei Fan 1
Gianlorenzo D'Angelo 1
Yanjie Fu 1
Yu Zheng 1
Saurav Sahay 1
Xiaowen Ding 1
Jörg Sander 1
Tanmoy Chakraborty 1
David Leake 1
Siyuan Liu 1
Chenguang Wang 1
Zhoujun Li 1
Neilzhenqiang Gong 1
Yi Chang 1
Qiang Wei 1
Maria Halkidi 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 Gleich 1
Steven Hoi 1
David Jensen 1
Tengfei Bao 1
Brook Wu 1
Glenn Fung 1
Zeeshan Syed 1
Kamalakar Karlapalem 1
Dale Schuurmans 1
Peer Kröger 1
Céline Robardet 1
Jean Boulicaut 1
Pradeep Tamma 1
Zengjian Hu 1
Boaz Ben-Moshe 1
Moshe Kam 1
Jieping Ye 1
Licong Cui 1
Xiaofeng Zhu 1
Dimitrios Mavroeidis 1
Neil Smalheiser 1
James Cheng 1
Ori Stitelman 1
Shachar Kaufman 1
Nikolaj Tatti 1
Leland Wilkinson 1
José Balcázar 1
Hockhee Ang 1
Steven Hoi 1
Weekeong Ng 1
Mengling Feng 1
Xiao Jiang 1
Lyle Ungar 1
Comandur Seshadhri 1
Luan Tang 1
Quanquan Gu 1
Franco Turini 1
Xintao Wu 1
Nick Duffield 1
Jianyong Wang 1
Chun Li 1
Feitony Liu 1
Petros Drineas 1
Sanjay Chawla 1
Jinpeng Wang 1
Arnau Prat-Pérez 1
Josep Larriba-Pey 1
Risa Myers 1
Brian Gallagher 1
Qingtian Zeng 1
John Hutchins 1
Taneli Mielikäinen 1
Ji Liu 1
Manuel Gomez-Rodriguez 1
Sethuraman Panchanathan 1
Abdullah Mueen 1
Yizhou Sun 1
Xiaofei He 1
Muthuramakrishnan Venkitasubramaniam 1
Victor Lee 1
Robert Kleinberg 1
Zhi Yang 1
Yafei Dai 1
Pierluigi Crescenzi 1
Zijun Yao 1
Maoying Qiao 1
Wei Bian 1
Ying Jin 1
Weiming Hu 1
Hiroshi Mamitsuka 1
Carlos Lorenzetti 1
Thomas Reichherzer 1
Dan Roth 1
Ephraim Korach 1
Jeffrey Ullman 1
Wenyuan Zhu 1
Kai Zheng 1
Zhongyuan Wang 1
Allon Percus 1
Xunhua Guo 1
Sitaram Asur 1
Jerry Kiernan 1
Kevin Yip 1
Wei Zheng 1
Zhenxing Wang 1
Ravi Konuru 1
Baoxing Huai 1
Hengshu Zhu 1
Nick Street 1
Pritam Gundecha 1
Lei Chen 1
Fan Guo 1
Edward Wild 1
Murat Kantarcıoğlu 1
John Guttag 1
Marc Plantevit 1
Jinlin Chen 1
Shantanu Godbole 1
Alin Dobra 1
Binay Bhattacharya 1
Zoran Obradović 1
Wangchien Lee 1
Sri Ravana 1
Alex Beutel 1
Shiqiang Yang 1
Bin Zhou 1
Anushka Anand 1
Yicheng Tu 1
Dan Simovici 1
Hao Wang 1
Siddharth Gopal 1
Madhav Jha 1
Alice Leung 1
Renato Assunção 1
Subhabrata Sen 1
Dino Ienco 1
Rosa Meo 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
Chihjen Lin 1
Zheng Wang 1
Thanawin Rakthanmanon 1
Jesin Zakaria 1
Kedar Bellare 1
Brandon Norick 1
Ming Ji 1
Yuval Elovici 1
Ming Lin 1
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
Zhanpeng Fang 1
Jing Peng 1
Yang Zhou 1
Xinjiang Lu 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
Dengyong Zhou 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
Anon Plangprasopchok 1
Shengrui Wang 1
Siyuan Liu 1
Ganesh Ramesh 1
A Patterson 1
Manolis Kellis 1
Patrick Hung 1
Carlos Castillo 1
Tianbing Xu 1
Sanmay Das 1
Amit Dhurandhar 1
Beechung Chen 1
Fedja Hadzic 1
Elizabeth Chang 1
Feiyu Xiong 1
Fei Wang 1
Shiqiang Tao 1
Guoqiang Zhang 1
Aminul Islam 1
Li Wan 1
Weekeong Ng 1
Sethuraman Panchanathan 1
Michael Mampaey 1
Yu Lei 1
Haojun Zhang 1
Limsoon Wong 1
Maria Sapino 1
Shipeng Yu 1
Zhiting Hu 1
Pedro Melo 1
Yuan Jiang 1
Matteo Riondato 1
Qinbao Song 1
Michele Coscia 1
Yi Wang 1
Jaideep Srivastava 1
João Gama 1
Charles Elkan 1
Carlos Guestrin 1
Naonori Ueda 1
Tomoharu Iwata 1
Qi Lou 1
Xifeng Yan 1
Julian McAuley 1
Bertil Schmidt 1
Yi Yang 1
Tao Mei 1
Quanzeng You 1
Kosuke Hashimoto 1
Nobuhisa Ueda 1
Jie Tang 1
Haiqin Yang 1
Aparna Varde 1
Ricardo Campello 1
Shuhui Wang 1
Qiang Qu 1
Bin Liu 1
Antonio Ortega 1
Pedro Vaz De Melo 1
Michael Houle 1
Dimitrios Gunopulos 1
Jeffrey Chan 1
Daxin Jiang 1
Muna Al-Razgan 1
Lei Zhang 1
Mohsen Bayati 1
Peilin Zhao 1
Raymond Wong 1
Ada Fu 1
Li Zheng 1
Noman Mohammed 1
Chao Liu 1
Jaideep Vaidya 1
Collin Stultz 1
Boleslaw Szymanski 1
Maguelonne Teisseire 1
Lini Thomas 1
Paolo Boldi 1
Sachindra Joshi 1
Tharam Dillon 1
Leonid Hrebien 1
Pei Yang 1
Li Li 1
Denian Yang 1
Zhishan Guo 1
Yunsing Koh 1
Silei Xu 1
Bo Liu 1
Wei Fan 1
Yixin Chen 1
Xuanhong Dang 1
Shumo Chu 1
Bingrong Lin 1
Francesco Bonchi 1
Luigi Pontieri 1
Kasim Candan 1
Sunil Vadera 1
S Upham 1
Thomas Porta 1
Hongzhi Yin 1
Jeffrey Erman 1
Ming Li 1
Joydeep Ghosh 1
Dora Erdős 1
Kaiyuan Zhang 1
Carlos Ordonez 1
James Cheng 1
Fosca Giannotti 1
U Kang 1
Peter Christen 1
Daniel Dunlavy 1
David Dominguez-Sal 1
Danai Koutra 1
Christos Doulkeridis 1
Steven Skiena 1
Hiroshi Motoda 1
Chris Volinsky 1
Andreas Krause 1
Hsiangfu Yu 1
Aditya Parameswaran 1
Binbin Lin 1
Johannes Gehrke 1
Christo Wilson 1
Ben Zhao 1
Hoangvu Dang 1
Fen Xia 1
Linlin Zong 1
Yijuan Lu 1
Feng Liu 1
Yufeng Wang 1
Ernest Garcia 1
Shamkant Navathe 1
Wei Fan 1
Rezwan Ahmed 1
Wei Wei 1
Ana Maguitman 1
Filippo Menczer 1
Foto Afrati 1
Rómer Rosales 1
Xiaofang Zhou 1
Xindong Wu 1
Fangtao Li 1
Junjie Wu 1
Duygu Ucar 1
Mustafa Bilgic 1
Ben Kao 1
David Cheung 1
Cheng Zeng 1
Atreya Srivathsan 1
Tong Sun 1
Songhua Xu 1
Yanchi Liu 1
Kun Liu 1
Duo Zhang 1
Dmitry Pavlov 1
Raymond Ng 1
Piotr Indyk 1
Christopher Carothers 1
Anne Laurent 1
Satyanarayana Valluri 1
Ashish Verma 1
Jérémy Besson 1
Raghu Ramakrishnan 1
Rong Ge 1
Byronju Gao 1
Yubao Wu 1
Maryam Tahani 1
Hamid Rabiee 1
Ying Wei 1
Li Tu 1
Saharon Rosset 1
Claudia Perlich 1
Seekiong Ng 1
Tuannhon Dang 1
Hong Xie 1
Ramana Kompella 1
Chengkai Li 1
Vasileios Kandylas 1
Salvatore Ruggieri 1
Jing Zhang 1
Rodrigo Alves 1
Juhua Hu 1
Yu Jin 1
Veerabhadran Baladandayuthapani 1
Giulio Rossetti 1
Timothy De Vries 1
Eric Xing 1
Albert Bifet 1
Xiaoming Li 1
Josep Brunat 1
Padhraic Smyth 1
Claudia Plant 1
Jiayu Pan 1
Jiang Bian 1
Brandon Westover 1
Eamonn Keogh 1
Ron Eyal 1
Avi Rosenfeld 1
Asaf Shabtai 1
Shifeng Weng 1
Junming Shao 1
Yllka Velaj 1
Xiaojun Chang 1
Lars Schmidt-Thieme 1
Michael Lyu 1
Dityan Yeung 1
Evangelos Papalexakis 1
Nicholas Sidiropoulos 1
George Karypis 1
Jilei Tian 1
Davoud Moulavi 1
Jun Yan 1
James Bezdek 1
Marimuthu Palaniswami 1
Jayavardhana Gubbi 1
Koji Hino 1
Masaru Kitsuregawa 1
Xiang Zhang 1
Jenwei Huang 1
Jianping Zhang 1
Manas Somaiya 1
Graham Cormode 1
James Bailey 1
Fernando Kuipers 1
Dick Epema 1
Linpeng Tang 1
Min Wang 1
Bin Li 1
Marc Maier 1
Lionel Ni 1

Affiliation Paper Counts
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
University of California, Los Angeles 1
National Taiwan University of Science and Technology 1
Oracle Corporation 1
Lanzhou University 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
University of Queensland 1
Curtin University of Technology, Perth 1
US Naval Academy 1
University of Roma La Sapienza 1
University of New Mexico 1
Institute of Mathematics and Informatics Lithuanian 1
Tongji University 1, Inc. 1
Harvard School of Engineering and Applied Sciences 1
Ariel University Center of Samaria 1
Siemens USA 1
Innopolis University 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
The University of North Carolina at Charlotte 1
University of South Florida Tampa 1
Valley Laboratory 1
University of Salford 1
Hong Kong Polytechnic University 1
Australian National University 1
University of Texas at Dallas 1
University of Vermont 1
Harvard University 1
University of Arizona 1
Nanjing University of Science and Technology 1
Washington University in St. Louis 1
HP Labs 1
Universidad Politecnica de Valencia 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 1
Zhejiang Wanli University 1
Aston University 1
Colorado School of Mines 1
University of Louisiana at Lafayette 1
John Carroll University 1
Radboud University Nijmegen 1
Brigham and Women's Hospital 1
University of Toronto 1
De Montfort University 1
Wright State University 1
Singapore Management University 1
Air Force Research Laboratory 1
University of West Florida 1
Universite Montpellier 2 Sciences et Techniques 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
Universite Claude Bernard Lyon 1 1
Lancaster University 1
Osaka University 1
University of Iowa 1
National University of Defense Technology China 1
Missouri University of Science and Technology 1
University of California, Berkeley 1
Shanghai Jiaotong University 1
Max Planck Institute for Informatics 2
Shandong University of Science and Technology 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
Soochow University 2
Brown 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
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
University of Tokyo 2
University Michigan Ann Arbor 2
Nokia Corporation 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
International Institute of Information Technology Hyderabad 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
Beihang University 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
Microsoft Corporation 3
University of California, Santa Barbara 3
Wuhan University 3
University of Alberta 3
Johannes Gutenberg University Mainz 3
Emory University 4
Institute for Infocomm Research, A-Star, Singapore 4
Google Inc. 4
Brookhaven National Laboratory 4
Universitat Politecnica de Catalunya 4
University of Sao Paulo 4
The University of Western Ontario 4
IBM Research 4
National University of Singapore 4
Athens University of Economics and Business 4
Monash University 4
Boston University 4
Massachusetts Institute of Technology 4
Sharif University of Technology 4
University of Pisa 4
Rutgers, The State University of New Jersey 4
Yahoo Research Barcelona 4
Aalto University 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
Purdue University 5
University of Turin 5
University of Antwerp 5
Renmin University of China 5
Sandia National Laboratories 5
New Jersey Institute of Technology 5
The University of North Carolina at Chapel Hill 5
University of Southern California 5
University of Texas System 5
Rutgers University-Newark Campus 6
Pennsylvania State University 6
Delft University of Technology 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 Minnesota Twin Cities 6
Yahoo Inc. 6
University of Texas at Austin 7
University of Florida 7
University of Science and Technology of China 7
University of Maryland 7
Microsoft Research 7
Ben-Gurion University of the Negev 7
University of California, Riverside 7
Federal University of Minas Gerais 7
University of Wisconsin Madison 7
Nanyang Technological University 8
Oregon State University 8
Xi'an Jiaotong University 8
Georgia Institute of Technology 8
Stony Brook University 8
Virginia Tech 8
Nanjing University 8
Stanford University 9
Peking University 9
Florida International University 9
IBM Thomas J. Watson Research Center 9
Yahoo Research Labs 9
University of Texas at Arlington 10
National Taiwan University 10
University of Melbourne 10
University of Illinois at Chicago 10
Hong Kong University of Science and Technology 11
Rensselaer Polytechnic Institute 11
Northwestern Polytechnical University China 12
Cornell University 12
University of Calabria 12
University of Technology Sydney 12
NEC Laboratories America, Inc. 15
Simon Fraser University 16
University of Illinois at Urbana-Champaign 18
Chinese University of Hong Kong 19
Tsinghua University 25
Carnegie Mellon University 32
Arizona State University 45

ACM Transactions on Knowledge Discovery from Data (TKDD)

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