Overview

The seventh SNA-KDD workshop is proposed as the seventh in a successful series of workshops on social network mining and analysis co-held with KDD, soliciting experimental and theoretical work on social network mining and analysis in both online and offline social network systems.

In recent years, social network research has advanced significantly, thanks to the prevalence of the online social websites and instant messaging systems as well as the availability of a variety of large-scale offline social network systems. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are increasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topological properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining social networks. These issues have important implications on community discovery, anomaly detection, trend prediction and can enhance applications in multiple domains such as information retrieval, recommendation systems, security and so on.

The past SNA-KDD workshops have achieved significant attentions from the world-wide researchers working in different aspects of social network analysis, including knowledge discovery and data mining in social network, social network modeling, multi-agent based social network simulation, complex generic network analysis and other related studies that can bring inspirations or be directly applied to social network analysis. Each year we received more than 30 submissions. The average acceptance rate is around 1/3.

Schedule Return to Top

Keynote Speaker 1 Return to Top

Jiawei Han, Abel Bliss Professor of Computer Science, University of Illinois at Urbana-Champaign

Title: From Social Networks to Heterogeneous Social and Information Networks: A Data Mining Perspective

Abstract: Many people treat social networks as homogeneous networks, modeled mainly as people network. Actually, people and informational objects are interconnected, forming gigantic, interconnected, integrated social and information networks. By structuring these data objects into multiple types, such networks become semi-structured heterogeneous social and information networks. Most real world applications that handle big data, including interconnected social media and social networks, medical information systems, online e-commerce systems, or database systems, can be structured into typed, heterogeneous social and information networks. For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks. Effective analysis of large-scale heterogeneous social and information networks poses an interesting but critical challenge.
    In this talk, we present a set of data mining scenarios in heterogeneous social and information networks and show that mining typed, heterogeneous networks is a new and promising research frontier in data mining research. Departing from many existing network models that view data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and can uncover surprisingly rich knowledge from interconnected data. This heterogeneous network modeling will lead to the discovery of a set of new principles and methodologies for mining interconnected data. The examples to be used in this discussion include (1) meta path-based similarity search, (2) rank-based clustering, (3) rank-based classification, (4) meta path-based link/relationship prediction, (5) relation strength-aware mining, as well as a few other recent developments. We will also point out some promising research directions and provide convincing arguments on that mining heterogeneous information networks could be a key to social intelligence mining.

Bio: Jiawei Han, Abel Bliss Professor of Computer Science, University of Illinois at Urbana-Champaign. He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 600 journal and conference publications. He has chaired or served on many program committees of international conferences, including PC co-chair for KDD, SDM, and ICDM conferences, and Americas Coordinator for VLDB conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and is serving as the Director of Information Network Academic Research Center supported by U.S. Army Research Lab. He is a Fellow of ACM and IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, 2009 IEEE Computer Society Wallace McDowell Award, and 2011 Daniel C. Drucker Eminent Faculty Award at UIUC. His book "Data Mining: Concepts and Techniques" has been used popularly as a textbook worldwide.

Keynote Speaker 2 Return to Top

Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology, Department of Computer Science, University of Illinois at Chicago

Title: Challenges and Advances on Social Network Mining

Abstract: Mining social network data has become an important and active research topic in the last decade, which has a wide variety of scientific and commercial applications. We first consider the survivability issue of communities. Among communities, we notice that some of them are magnetic to people. A magnet community is such a community that attracts significantly more people's interests and attentions than other communities of similar topics. We will study the magnet community identification problem. Next we consider the cascading effect of nodes in a network. This is sometime referred to as the "too big to fail" problem in the financial world, describing certain financial institutions which are so large and so interconnected that their failure will be disastrous to the economy, and which therefore must be supported by government when they face difficulty. We call such high impact entities shakers. To discover shakers, we introduce the concept of a cascading graph to capture the causality relationships among evolving entities over some period of time, and then infer shakers from the graph. In a cascading graph, nodes represent entities and weighted links represent the causality effects. Finally, we consider how to capture anomaly behavior in a network. Specifically, we look into the spam review detection problem. Online reviews provide valuable information about products and services to consumers. However, spammers are joining the community trying to mislead readers by writing fake reviews. We propose a novel concept of a heterogeneous review graph to capture the relationships among reviewers, reviews and stores that the reviewers have reviewed. We explore how interactions between nodes in this graph can reveal the cause of spam and propose an iterative model to identify suspicious reviewers.

Bio: Philip S. Yu is currently a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information Technology. He spent most of his career at IBM Thomas J. Watson Research Center and was manager of the Software Tools and Techniques group. His research interests include data mining, privacy preserving data publishing, data stream, Internet applications and technologies, and database systems. Dr. Yu has published more than 740 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents.
    Dr. Yu is a Fellow of the ACM and the IEEE. He is the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data. He is on the steering committee of the IEEE Conference on Data Mining and ACM Conference on Information and Knowledge Management and was a member of the IEEE Data Engineering steering committee. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He had also served as an associate editor of ACM Transactions on the Internet Technology and Knowledge and Information Systems. Dr. Yu received an IEEE Computer Society 2013 Technical Achievement Award for "pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data" and a Research Contributions

Invited Speaker Return to Top

Ee-Peng Lim, SMU Director, Living Analytics Research Centre, School of Information Systems, Singapore Management University

Title: Living Analytics: Challenges and Opportunities

Bio: Ee-Peng Lim is a professor at the School of Information Systems of Singapore Management University (SMU). He received Ph.D. from the University of Minnesota, Minneapolis in 1994 and B.Sc. in Computer Science from National University of Singapore. His research interests include social network and web mining, information integration, and digital libraries. He is the principal investigator and co-PI of several research projects funded by A*Star, National Research Foundation (NRF) of Singapore, and DSO National Labs. He is currently an Associate Editor of the ACM Transactions on Information Systems (TOIS), Information Processing and Management (IPM), Social Network Analysis and Mining, Journal of Web Engineering (JWE), IEEE Intelligent Systems, International Journal of Digital Libraries (IJDL) and International Journal of Data Warehousing and Mining (IJDWM). He was a member of the ACM Publications Board until December 2012. He serves on the Steering Committee of the International Conference on Asian Digital Libraries (ICADL), Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), and International Conference on Social Informatics.

Table of Contents Return to Top

Full Papers

Finding Contexts of Social Influence in Online Social Networks
Jennifer H. Nguyen(University College London, UK)
Bo Hu(Simon Fraser University, Canada)
Stephan Günnemann(Carnegie Mellon University, USA)
Martin Ester(Simon Fraser University, Canada)

ProfileRank: Finding Relevant Content and Influential Users based on Information Diffusion
Arlei Silva (Universidade Federal de Minas Gerais)
Sara Guimarães (Universidade Federal de Minas Gerais)
Wagner Meira Jr. (Universidade Federal de Minas Gerais)
Mohammed Zaki (Rensselaer Polytechnic Institute)

Network Flows and the Link Prediction Problem
Kanika Narang (Indraprastha Institute of Information Technology)
Kristina Lerman (USC Information Sciences)
Ponnurangam Kumaraguru (Indraprastha Institute of Information Technology Delhi, India)

Epidemiological Modeling of News and Rumors on Twitter
Fang Jin (Virginia Tech, Blacksburg)
Edward Dougherty (Virginia Tech, Blacksburg)
Parang Saraf (Virginia Tech, Blacksburg)
Yang Cao (Virginia Tech, Blacksburg)
Naren Ramakrishnan (Virginia Tech, Blacksburg)

Modeling Direct and Indirect Influence across Heterogeneous Social Networks
Minkyoung Kim (The Australian National University)
David Newth (Commonwealth Scientific and Industrial Research Organisati)
Peter Christen (The Australian National University)

Structure and Attributes Community Detection: Comparative Analysis of Composite, Ensemble and Selection Metho
Haithum Elhadi (Computer Science Department Illinois Institute of Technology, Chicago)
Gady Agam (Computer Science Department Illinois Institute of Technology, Chicago)


Short Paper

Community Finding within the Community Set Space
Jerry Scripps (Grand Valley State University)
Christian Trefftz (Grand Valley State University)

Analysis and Identification of Spamming Behaviors in Sina Weibo Microblog
Chengfeng Lin (Shanghai Jiaotong University)
Yi Zhou (Shanghai Jiaotong University)
Kai Chen (Shanghai Jiaotong University)
Jianhua He (Aston University)
Li Song (Shanghai Jiaotong University)
Xiaokang Yang (Shanghai Jiaotong University)

CUT: Community Update and Tracking in Dynamic Social Networks
Hao-Shang Ma (National Cheng Kung University, Taiwan)
Jen-Wei Huang (National Cheng Kung University, Taiwan)

Leveraging Candidate Popularity On Twitter To Predict Election Outcome
Manish Gaurav (Raytheon BBN Technologies)
Anoop Kumar (Raytheon BBN Technologies)
Amit Srivastava (Raytheon BBN Technologies)
Scott Miller (Raytheon BBN Technologies)

Mixing Bandits: A Recipe for Improved Cold-Start Recommendations in a Social Network
Stéphane Caron (University of Tokyo)
Smriti Bhagat (Technicolor)

The User’s Communication Patterns on A Mobile Social Network Site
Youngsoo Kim (Singapore Management University, Singapore)

Twitter Volume Spikes: Analysis and Application in Stock Trading
Yuexin Mao (University of Connecticut)
Wei Wei (FinStats.com)
Bing Wang (University of Connecticut)

Customized Reviews for Small User-Databases using Iterative SVD and Content Based Filtering
Jon Gregg (Georgia Tech, Atlanta)
Nitin Jain (Georgia Tech, Atlanta)

Organizers Return to Top

Organizing Committee

• Feida Zhu, Assistant Professor, Singapore Mangement University, Singapore
• Qi He, Research Staff Member, IBM Almaden Research Center, US
• Rong Yan, Research Scientist, Facebook, US
• John Yen, University Professor, Pennsylvania State University, US
• Juan Du, Research Engineer, Singapore Mangement University, Singapore (Web Chair)

Program Committee

• Kuiyu Chang (Nanyang Technological University)
• Ed Chi (Google)
• Ee-Peng Lim (Singapore Management University)
• Dunja Mladenic (J. Stefan Institute Slovenia)
• Shengli Victor Sheng (University of Central Arkansas)
• Ambuj Singh (University of California at Santa Barbara)
• Hanghang Tong (IBM T.J. Watson Research)
• Jianshu Weng (HP Labs)
• Xifeng Yan (University of California at Santa Barbara)
• Philip Yu (University of Illinois, Chicago)
• Weining Qian (East China Normal University)
• Aixin Sun (Nanyang Technological University)
• Jing Jiang (Singapore Management University)
• Aek Palakorn Achananuparp (Singapore Management University)
• Jie Tang (Tsinghua University, China)
• Zhenhui Li (Penn State University)
• Jing Gao (University at Buffalo)
• Yizhou Sun (Northeastern University)
• James Cheng (The Chinese University of Hong Kong)
• Hady W. Lauw (Singapore Management University)
• Victor E. Lee (John Carroll University)

Sponsors Return to Top