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ACM Transactions on

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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)

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)

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)

Adaptive Cluster Tendency Visualization and Anomaly Detection for Streaming Data

The growth in pervasive network infrastructure called the Internet of Things (IoT) enables a wide... (more)

Exploiting Viral Marketing for Location Promotion in Location-Based Social Networks

With the explosion of smartphones and social network services, location-based social networks (LBSNs) are increasingly seen as tools for businesses... (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.

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Forthcoming Articles
Spatial Prediction for Multivariate Non-Gaussian Data

With the ever increasing volume of geo-referenced datasets, there is a real need for better statistical estimation and prediction techniques for spatial analysis. Most existing approaches focus on predicting multivariate Gaussian spatial processes, but as the data may consist of non-Gaussian (or mixed type) variables, this creates two challenges: 1) how to accurately capture the dependencies among different data types, both Gaussian and non-Gaussian; and 2) how to efficiently predict multivariate non-Gaussian spatial processes. In this paper, we propose a generic approach for predicting multiple response variables of mixed types. The proposed approach accurately captures cross-spatial dependencies among response variables and reduces the computational burden by projecting the spatial process to a lower dimensional space with knot-based techniques. Efficient approximations are provided to estimate posterior marginals of latent variables for the predictive process and extensive experimental evaluations based on both simulation and real-life datasets are provided to demonstrate the effectiveness and efficiency of this new approach.

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.

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 paper, we go one step further to understand the query from the view of head-modifier analysis. For example, given the query popular iphone 5 smart cover, instead of using coarse-grained semantic classes (e.g., find electronic product), we interpret that smart cover is the head or the intent of the query and iphone 5 is its modifier. Query head-modifier detection can help search engines to obtain particularly relevant content, which is also important for applications such as ads matching and query recommendation. We introduce an unsupervised semantic approach for query head-modifier detection. First, we mine a large number of instance level head-modifier pairs from search log. Then, we develop a conceptualization mechanism to generalize the instance level pairs to concept level. Finally, we derive weighted concept patterns that are concise, accurate, and have strong generalization power in head-modifier detection. The developed mechanism has been used in production for search relevance and ads matching. We use extensive experiment results to demonstrate the effectiveness of our approach.

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 hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.

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.

Solving Inverse Frequent Itemset Mining with Infrequency Constraints via Large-Scale Linear Programs

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees.

Visual Analysis of Brain Networks using Sparse Regression Models

Studies of the human brain network are becoming increasingly popular in the fields of neuroscience, computer science and neurology. Despite this rapidly growing line of research, gaps remain on the intersection of advanced data analytics, interactive visual representation and the human intelligence - all needed to advance our understanding of human brain networks. This paper tackles this challenge by exploring the design space of visual analytics. Notably, we propose an integrated framework to orchestrate computational models with comprehensive data visualizations on the human brain network. The framework targets two fundamental tasks: the visual exploration of multi-label brain networks and the visual comparison among brain networks across different subject groups. During the first task, we propose a novel interactive user interface to visualize sets of labeled brain networks; in our second task, we introduce sparse regression models to select discriminative edge features from the brain network to facilitate the comparison. Through user studies and quantitative experiments, both methods are shown to greatly improve the visual comparison performance. Finally, real-world case studies with domain experts demonstrate the utility and effectiveness of our framework to analyze reconstructions of human brain connectivity maps. The perceptually optimized visualization design and the feature selection model calibration are shown to be the key to our significant findings.

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.

Parallel Field Ranking

Memory-efficient and Accurate Sampling for Counting Local Triangles in Graph Streams: From Simple to Multigraphs

How can we estimate local triangle counts accurately in a graph stream without storing the whole graph? How to handle duplicated edges in local triangle counting for graph stream? The local triangle counting which counts triangles for each node in a graph is a very important problem with wide applications in social network analysis, anomaly detection, web mining, etc. In this paper, we propose algorithms for local triangle counting in a graph stream based on edge sampling: MASCOT for a simple graph, and MULTIBMASCOT and MULTIWMASCOT for a multigraph. To develop MASCOT, we first present two naive local triangle counting algorithms in a graph stream, called MASCOT-C and MASCOT-A. MASCOT-C is based on constant edge sampling, and MASCOT-A improves its accuracy by utilizing more memory spaces. MASCOT achieves both accuracy and memory-efficiency of the two algorithms by unconditional triangle counting for a new edge, regardless of whether it is sampled or not. Extending the idea to a multigraph, we develop two algorithms MULTIBMASCOT and MULTIWMASCOT. MULTIBMASCOT enables local triangle counting on the corresponding simple graph of a streamed multigraph without explicit graph conversion; MULTIWMASCOT considers repeated occurrences of an edge as its weight and counts each triangle as the product of its three edge weights. Our proposed algorithms require only one parameter of edge sampling probability, in contrast to the existing algorithm which requires prior knowledge on the target graph and appropriately set parameters. Through extensive experiments, we show that for the same number of edges sampled, MASCOT provides the best accuracy compared to the existing algorithm as well as MASCOT-C and MASCOT-A. We also demonstrate that MULTIBMASCOT on a multigraph is comparable to MASCOT-C on the counterpart simple graph, and MULTIWMASCOT becomes more accurate for higher degree nodes. Thanks to MASCOT, we also discover interesting anomalous patterns in real graphs, including core-peripheries in the web, a bimodal call pattern in a phone call history, and intensive collaboration in DBLP.

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.

Modeling Buying Motives for Personalized Product Bundle Recommendation

Product bundling is a marketing strategy that offers several products/items for sale as one bundle. While the bundling strategy has been widely used, less efforts have been made to understand how items should be bundled with respect to consumers preferences and buying motives for product bundles. This paper investigates the relationships between the items that are bought together within a product bundle. To that end, each purchased product bundle is formulated as a bundle graph with items as nodes and the associations between pairs of items in the bundle as edges. The relationships between items can be analyzed by the formation of edges in bundle graphs, which can be attributed to the associations of feature aspects. Then, a probabilistic model BPM (Bundle Purchases with Motives) is proposed to capture the composition of each bundle graph, with two latent factors node-type and edge-type introduced to describe the feature aspects and relationships respectively. Furthermore, based on the preferences inferred from the model, an approach for recommending items to form product bundles is developed by estimating the probability that a consumer would buy an associative item together with the item already bought in the shopping cart. Finally, experimental results on real-world transaction data collected from well-known shopping sites show the effectiveness advantages of the proposed approach over other baseline methods. Moreover, the experiments also show that the model explains consumers buying motives for product bundles in terms of different node-types and edge-types.

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.

Batch Mode Active Sampling based on Marginal Probability Distribution Matching

Life Cycle 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 keywords in a short time interval. Detecting buzz not only provides useful insights into the information propagation mechanism, but also plays an essential role in preventing malicious rumors. However, buzz modeling is a challenging task because a buzz time-series often exhibits sudden spikes and heavy tails, which fails most existing time-series models. In this paper, we propose novel buzz modeling approaches which capture the rise and fade temporal patterns via Product Life Cycle (PLC) model, a classical concept in economics. More specifically, we propose to model multiple peaks in buzz time-series with PLC mixture or PLC group mixture, and develop a probabilistic graphical model (K-MPLC) to automatically discover inherent life cycle patterns within a collection of buzzes. Furthermore, we effectively utilize the model parameters of PLC mixture or PLC group mixture for burst prediction. Our experiment results show that our proposed methods significantly outperform existing leading approaches on buzz clustering and buzz type prediction.

Differentially-Private Multidimensional Data Publishing

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

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Bibliometrics

Publication Years 2007-2016
Publication Count 269
Citation Count 2197
Available for Download 269
Downloads (6 weeks) 3413
Downloads (12 Months) 29490
Downloads (cumulative) 182128
Average downloads per article 677
Average citations per article 8
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)
Sethuraman Panchanathan ACM Senior Member (2009)
Jian Pei ACM Senior Member (2007)
Domenico Sacca ACM Senior Member (2007)
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
Tao Li 5
Philip Yu 4
Jian Pei 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
Mingsyan Chen 3
Jilles Vreeken 3
Yun Chi 3
Evimaria Terzi 3
Yasushi Sakurai 3
Yihong Gong 3
Lei Tang 3
Bin Guo 3
Hong Cheng 3
Dingding Wang 3
Fabio Fassetti 3
Fabrizio Angiulli 3
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
Antônio Loureiro 2
Hanghang Tong 2
Xiang Zhang 2
Daniel Kifer 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
Belle Tseng 2
Enhong CHEN 2
Qi Liu 2
Vivekanand Gopalkrishnan 2
Jie Tang 2
Xiaoli Fern 2
Eugene Agichtein 2
Sanjay Ranka 2
Carlotta Domeniconi 2
Jiliang Tang 2
Dantong Yu 2
Charalampos Tsourakakis 2
Srinivasan Parthasarathy 2
Hari Sundaram 2
Spiros Papadimitriou 2
Joydeep Ghosh 2
Martin Ester 2
Wei Fan 2
Dino Pedreschi 2
Pinghui Wang 2
Hao Huang 2
Hong Qin 2
Kristina Lerman 2
Yehuda Koren 2
Heikki Mannila 2
Panayiotis Tsaparas 2
Chengqi Zhang 2
Zhu Wang 2
Jianhui Chen 2
Fabrizio Sebastiani 2
Yu Zhang 2
Arthur Zimek 2
Michalis Vazirgiannis 2
Junzhou Zhao 2
Xiaohong Guan 2
Geoffrey Webb 2
Panagis Magdalinos 2
Indrajit Bhattacharya 2
Qiang Yang 2
Neil Shah 1
Alexander Ihler 1
Masahiro Kimura 1
Kaiwei Chang 1
Forrest Briggs 1
Gustavo Batista 1
Qiang Zhu 1
Philip Yu 1
Jon Kleinberg 1
Jure Leskovec 1
Maya Bercovitch 1
Stefan Kramer 1
Huaimin Wang 1
Qiang You 1
Miao Tian 1
Luke McDowell 1
Qi Tian 1
Jennifer Neary 1
Naren Ramakrishnan 1
Minoru Kanehisa 1
Irwin King 1
Ling Liu 1
Huilei He 1
Hua Wang 1
Fei Zou 1
Virgílio Almeida 1
Christos Faloutsos 1
Nitin Agarwal 1
Laiwan Chan 1
S Muthukrishnan 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
Cheukkwong Lee 1
Chris Clifton 1
Mohammed Zaki 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
Yada Zhu 1
Tina Eliassi-Rad 1
Chen Chen 1
Leman Akoglu 1
Gianluigi Greco 1
Francesco Gullo 1
Guimei Liu 1
Min Ding 1
Jennifer Neville 1
Gensheng Zhang 1
Wenchih Peng 1
Zekai Gao 1
Laura Smith 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
Aditya Menon 1
Zhongfei Zhang 1
Matthew Rowe 1
Edward Chang 1
ChengXiang Zhai 1
Dong Xin 1
Kazumi Saito 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
Tingting Gao 1
Dityan Yeung 1
Longjie Li 1
Bruno Abrahão 1
Xiaolin Wang 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
Xiaodan Song 1
Wei Wang 1
Yasuhiro Fujiwara 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
Chris Ding 1
Jing Zhang 1
Benoît Dumoulin 1
Xiuyao Song 1
John Gums 1
Yin Zhang 1
Yunxin Zhao 1
Zhongfei Zhang 1
Jude Shavlik 1
Beilun Wang 1
Zhitao Wang 1
Chihya Shen 1
Jingrui He 1
Ali Hemmatyar 1
Wei Cheng 1
Meng Jiang 1
Peng Cui 1
Yangqiu Song 1
Yi Zhen 1
Qian Sun 1
Sibel Adalı 1
Xiaohui Lu 1
Domenico Saccà 1
Francesco Lupia 1
Nima Mirbakhsh 1
Antti Ukkonen 1
Xindong Wu 1
Yao Wu 1
Zheng Wang 1
Bin Cui 1
Juanzi Li 1
Johannes Schneider 1
Qingyan Yang 1
Patrick Haffner 1
Zhili Zhang 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
Yulan He 1
João Duarte 1
John Frenzel MD 1
Hua Duan 1
Joshua Vogelstein 1
Yandong Liu 1
Qiaozhu Mei 1
Takeshi Yamada 1
Suresh Iyengar 1
Jiawei Han 1
Ashwin Machanavajjhala 1
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
Siyuan Liu 1
Maria Halkidi 1
Luming Zhang 1
Lei Zou 1
Jian Wang 1
Manos Papagelis 1
Ruud Van De Bovenkamp 1
Clyde Giles 1
Wei Peng 1
David Gleich 1
David Jensen 1
Steven Hoi 1
Tengfei Bao 1
Brook Wu 1
Glenn Fung 1
Zeeshan Syed 1
Kamalakar Karlapalem 1
Dale Schuurmans 1
Dimitrios Mavroeidis 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
Neil Smalheiser 1
Ori Stitelman 1
James Cheng 1
Shachar Kaufman 1
Nikolaj Tatti 1
Leland Wilkinson 1
José Balcázar 1
Steven Hoi 1
Weekeong Ng 1
Hockhee Ang 1
Mengling Feng 1
Xiao Jiang 1
Tanmoy Chakraborty 1
Neilzhenqiang Gong 1
Lyle Ungar 1
Franco Turini 1
Comandur Seshadhri 1
Luan Tang 1
Quanquan Gu 1
Xintao Wu 1
Feitony Liu 1
Chun Li 1
Jianyong Wang 1
Petros Drineas 1
Nick Duffield 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
Yafei Dai 1
Victor Lee 1
Robert Kleinberg 1
Zhi Yang 1
Pierluigi Crescenzi 1
Zijun Yao 1
Weiming Hu 1
Maoying Qiao 1
Wei Bian 1
Ying Jin 1
Hiroshi Mamitsuka 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
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
Hao Wang 1
Dan Simovici 1
Wenyuan Zhu 1
Kai Zheng 1
Allon Percus 1
Siddharth Gopal 1
Madhav Jha 1
Alice Leung 1
Renato Assunção 1
Rosa Meo 1
Dino Ienco 1
Subhabrata Sen 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
Chihjen Lin 1
Seungil Huh 1
Chojui Hsieh 1
Zheng Wang 1
Thanawin Rakthanmanon 1
Jesin Zakaria 1
Kedar Bellare 1
Brandon Norick 1
Jiawei Han 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
Dengyong Zhou 1
Ming Zhang 1
Biru Dai 1
Divesh Srivastava 1
Zhenjie Zhang 1
Hungleng Chen 1
Liang Hong 1
Venu Satuluri 1
Hunghsuan Chen 1
Rose Yu 1
Yan Liu 1
Yao Zhang 1
Aisling Kelliher 1
Paul Castro 1
Lian Duan 1
Bruno Ribeiro 1
Siyuan Liu 1
Ganesh Ramesh 1
Manolis Kellis 1
Anon Plangprasopchok 1
Shengrui Wang 1
Patrick Hung 1
A Patterson 1
Carlos Castillo 1
Tianbing Xu 1
Sanmay Das 1
Beechung Chen 1
Amit Dhurandhar 1
Fedja Hadzic 1
Elizabeth Chang 1
Aminul Islam 1
Feiyu Xiong 1
Guoqiang Zhang 1
Fei Wang 1
Shiqiang Tao 1
Li Wan 1
Weekeong Ng 1
Sethuraman Panchanathan 1
Yu Lei 1
Michael Mampaey 1
Limsoon Wong 1
Haojun Zhang 1
Maria Sapino 1
Animesh Mukherjee 1
Sriram Srinivasan 1
Niloy Ganguly 1
Sanjukta Bhowmick 1
Lingjyh Chen 1
Linhong Zhu 1
Shipeng Yu 1
Zhiting Hu 1
Yuan Jiang 1
Pedro Melo 1
Qinbao Song 1
Matteo Riondato 1
Michele Coscia 1
Yi Wang 1
Charles Elkan 1
Jaideep Srivastava 1
João Gama 1
Carlos Guestrin 1
Tomoharu Iwata 1
Naonori Ueda 1
Qi Lou 1
Wei Fan 1
Xifeng Yan 1
Julian McAuley 1
Bertil Schmidt 1
Yi Yang 1
Quanzeng You 1
Tao Mei 1
Nobuhisa Ueda 1
Kosuke Hashimoto 1
Jie Tang 1
Haiqin Yang 1
Aparna Varde 1
Ricardo Campello 1
Shuhui Wang 1
Pedro Vaz De Melo 1
Jeffrey Chan 1
Michael Houle 1
Dimitrios Gunopulos 1
Muna Al-Razgan 1
Daxin Jiang 1
Lei Zhang 1
Mohsen Bayati 1
Raymond Wong 1
Ada Fu 1
Peilin Zhao 1
Li Zheng 1
Noman Mohammed 1
Chao Liu 1
Jaideep Vaidya 1
Collin Stultz 1
Paolo Boldi 1
Boleslaw Szymanski 1
Maguelonne Teisseire 1
Lini Thomas 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
Yixin Chen 1
Xuanhong Dang 1
Shumo Chu 1
Luigi Pontieri 1
Bingrong Lin 1
Francesco Bonchi 1
Wei Ding 1
Kasim Candan 1
Bin Liu 1
Sunil Vadera 1
S Upham 1
Thomas Porta 1
Hongzhi Yin 1
Ming Li 1
Jeffrey Erman 1
Dora Erdős 1
Joydeep Ghosh 1
Kaiyuan Zhang 1
Carlos Ordonez 1
Fosca Giannotti 1
James Cheng 1
U Kang 1
Peter Christen 1
Daniel Dunlavy 1
Christos Doulkeridis 1
David Dominguez-Sal 1
Steven Skiena 1
Danai Koutra 1
Hiroshi Motoda 1
Chris Volinsky 1
Andreas Krause 1
Hsiangfu Yu 1
Aditya Parameswaran 1
Binbin Lin 1
Johannes Gehrke 1
Ben Zhao 1
Christo Wilson 1
Hoangvu Dang 1
Fen Xia 1
Linlin Zong 1
Yijuan Lu 1
Feng Liu 1
Yufeng Wang 1
Ernest Garcia 1
Shamkant Navathe 1
Wei Fan 1
Rezwan Ahmed 1
Wei Wei 1
Duygu Ucar 1
Mustafa Bilgic 1
Ben Kao 1
David Cheung 1
Christopher Leckie 1
Cheng Zeng 1
Atreya Srivathsan 1
Tong Sun 1
Songhua Xu 1
Yanchi Liu 1
Kun Liu 1
Duo Zhang 1
Raymond Ng 1
Dmitry Pavlov 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
Hamid Rabiee 1
Yubao Wu 1
Maryam Tahani 1
Ying Wei 1
Li Tu 1
Saharon Rosset 1
Claudia Perlich 1
Tuannhon Dang 1
Seekiong Ng 1
Kui Yu 1
Hong Xie 1
Ramana Kompella 1
Chengkai Li 1
Xiaofang Zhou 1
Junjie Wu 1
Vasileios Kandylas 1
Salvatore Ruggieri 1
Juhua Hu 1
Jing Zhang 1
Rodrigo Alves 1
Yu Jin 1
Veerabhadran Baladandayuthapani 1
Giulio Rossetti 1
Timothy De Vries 1
Eric Xing 1
Albert Bifet 1
Xiaoming Li 1
Josep Brunat 1
Jiang Bian 1
Padhraic Smyth 1
Jiayu Pan 1
Claudia Plant 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
Jilei Tian 1
George Karypis 1
Davoud Moulavi 1
Qiang Qu 1
Koji Hino 1
Xiang Zhang 1
Masaru Kitsuregawa 1
Jenwei Huang 1
James Bailey 1
Jianping Zhang 1
Manas Somaiya 1
Graham Cormode 1
Fernando Kuipers 1
Dick Epema 1
Linpeng Tang 1
Min Wang 1
Marc Maier 1
Bin Li 1
William Street 1
Lionel Ni 1
Mohamed Bouguessa 1
Mingxi Wu 1
Benjamin Fung 1
Ye Chen 1
John Canny 1
Luca Becchetti 1
Keli Xiao 1
Dominique Laurent 1
Yeowwei Choong 1
Ying Cui 1
Meghana Deodhar 1
Bo Long 1
Hans Kriegel 1
Gunjan Gupta 1
Ling Feng 1
Diana Inkpen 1
Dawei Zhou 1
Hongxia Yang 1
Haoda Fu 1
Jingrui He 1
Liming Chen 1
Michalis Faloutsos 1
Maryam Ramezani 1
Wei Wang 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
Yang Liu 1
Chunxiao Xing 1
Dechuan Zhan 1
Michail Vlachos 1
Ruggero Pensa 1
Saurabh Paul 1
Jose Hern´ndez-Orallo 1
Rainer Gemulla 1
Guangtao Wang 1
Xueying Zhang 1
Eli Upfal 1
Yiping Ke 1
Evrim Acar 1
Yang Zhou 1
Charu Aggarwal 1
Ben London 1
Jirong Wen 1
Joseph Ruiz Md 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
Curtin University of Technology, Perth 1
US Naval Academy 1
University of Roma La Sapienza 1
University of New Mexico 1
Saarland University 1
Institute of Mathematics and Informatics Lithuanian 1
Amazon.com, Inc. 1
Harvard School of Engineering and Applied Sciences 1
Ariel University Center of Samaria 1
Siemens USA 1
Microsoft Research Asia 1
Innopolis University 1
Ryukoku University 1
California State University Fullerton 1
National Research Institute of Science and Technology for Environment and Agriculture 1
University of Michigan 1
Anhui University 1
University of Ontario Institute of Technology 1
Universite de Cergy-Pontoise 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
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
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
Beihang University 1
Indian Institute of Science 1
Zhejiang Wanli University 1
Aston University 1
Colorado School of Mines 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
IBM 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
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
Brown University 2
Montclair State University 2
Hong Kong Baptist University 2
Renmin University of China 2
University of California, Davis 2
Drexel University 2
University of Texas M. D. Anderson Cancer Center 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
University of Virginia 2
Industrial Technology Research Institute of Taiwan 2
University of Massachusetts Boston 2
University of Tokyo 2
University Michigan Ann Arbor 2
Nokia 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
University of Queensland 2
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 2
Qatar Computing Research institute 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
University of Sao Paulo 3
The University of British Columbia 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 Sydney 3
Microsoft 3
University of Melbourne 3
University of California, Santa Barbara 3
Wuhan University 3
University of Southern California 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
The University of Western Ontario 4
IBM Research 4
University of Antwerp 4
National University of Singapore 4
Athens University of Economics and Business 4
Monash University 4
Boston University 4
Massachusetts Institute of Technology 4
Ben-Gurion University of the Negev 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
Kyoto University 5
University of Turin 5
Oregon State University 5
Sandia National Laboratories 5
New Jersey Institute of Technology 5
The University of North Carolina at Chapel Hill 5
Rutgers University-Newark Campus 6
Pennsylvania State University 6
Delft University of Technology 6
AT&T Laboratories Florham Park 6
University of Massachusetts Amherst 6
Nippon Telegraph & Telephone 6
Ludwig Maximilian University of Munich 6
University of Minnesota Twin Cities 6
Yahoo Inc. 6
University of Florida 7
Peking University 7
University of Science and Technology of China 7
University of Maryland 7
Microsoft Research 7
University of California, Riverside 7
Federal University of Minas Gerais 7
University of Wisconsin Madison 7
Nanyang Technological University 8
Stanford University 8
University of Texas at Austin 8
Xi'an Jiaotong University 8
Georgia Institute of Technology 8
Stony Brook University 8
Virginia Tech 8
Nanjing University 8
Yahoo Research Labs 8
Florida International University 9
IBM Thomas J. Watson Research Center 9
National Taiwan University 10
Hong Kong University of Science and Technology 10
University of Illinois at Chicago 10
Rensselaer Polytechnic Institute 11
Northwestern Polytechnical University China 12
Cornell University 12
University of Calabria 12
University of Technology Sydney 12
Simon Fraser University 14
University of Texas at Arlington 15
NEC Laboratories America, Inc. 15
University of Illinois at Urbana-Champaign 16
Chinese University of Hong Kong 19
Tsinghua University 21
Carnegie Mellon University 32
Arizona State University 45

ACM Transactions on Knowledge Discovery from Data (TKDD) Archive

2016

Volume 11 Issue 2, December 2016  Issue-in-Progress
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 4, October 2010
Volume 4 Issue 3, 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
 
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