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Workshop Program    Top

Workshop Schedule at a Glance

August 11, 2013 Sunday

08:45-10:00

Openning and award ceremony

Keynote Speech 1: From Social Networks to Heterogeneous Social and Information Networks: A Data Mining Perspective
Jiawei Han

10:00-10:30

Coffee break

10:30-11:30

Keynote Speech 2: Challenges and Advances on Social Network Mining
Philip S. Yu

11:30-12:00

Invited Talk: Living Analytics: Challenges and Opportunities
Ee-Peng Lim

12:00-13:00

Lunch

13:00-14:00

Poster Session for all the accepted papers

14:00-15:30

Session 1:

    Full Paper:

  • Finding Contexts of Social Influence in Online Social Networks
    Jennifer H. Nguyen, Bo Hu, Stephan GŁnnemann and Martin Ester
  • ProfileRank: Finding Relevant Content and Influential Users based on Information Diffusion
    Arlei Silva, Sara Guimar„es, Wagner Meira Jr. and Mohammed Zaki
  • Network Flows and the Link Prediction Problem
    Kanika Narang, Kristina Lerman and Ponnurangam Kumaraguru

  • Short Paper:

  • Twitter Volume Spikes: Analysis and Application in Stock Trading
    Yuexin Mao, and Wei Wei and Bing Wang
  • Analysis and Identification of Spamming Behaviors in Sina Weibo Microblog
    Chengfeng Lin, Yi Zhou, Kai Chen, Jianhua He, Li Song and Xiaokang Yang
  • CUT: Community Update and Tracking in Dynamic Social Networks
    Hao-Shang Ma and Jen-Wei Huang
  • Leveraging Candidate Popularity On Twitter To Predict Election Outcome
    Manish Gaurav, Anoop Kumar, Amit Srivastava and Scott Miller

15:30-16:00

Coffee break

16:00-17:30

Session 2:

    Full Paper:

  • Epidemiological Modeling of News and Rumors on Twitter
    Fang Jin, Edward Doughertyu, Parang Saraf, Yang Cao and Naren Ramakrishnan
  • Modeling Direct and Indirect Influence across Heterogeneous Social Networks
    Minkyoung Kim, David Newth and Peter Christen
  • Structure and Attributes Community Detection: Comparative Analysis of Composite, Ensemble and Selection Methods
    Haithum Elhadi and Gady Agam

  • Short Paper:

  • Mixing Bandits: A Recipe for Improved Cold-Start Recommendations in a Social Network
    Stťphane Caron and Smriti Bhagat
  • The User's Communication Patterns on A Mobile Social Network Site
    Youngsoo Kim
  • Community Finding within the Community Set Space
    Jerry Scripps and Christian Trefftz
  • Customized Reviews for Small User-Databases using Iterative SVD and Content Based Filtering
    Jon Gregg and Nitin Jain
Keynotes    Top

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

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

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.


TITLE: Challenges and Advances on Social Network Mining

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

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


TITLE: Living Analytics: Challenges and Opportunities

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

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.