Welcome to ASONAM 2013

The 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

The study of social networks originated in social and business communities. In recent years, social network research has advanced significantly; the development of sophisticated techniques for Social Network Analysis and Mining (SNAM) has been highly influenced by the online social Web sites, email logs, phone logs and instant messaging systems, which are widely analyzed using graph theory and machine learning techniques. People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of SNAM in academia, politics, homeland security and business. This follows the pattern of known entities of our society that have evolved into networks in which actors are increasingly dependent on their structural embedding.

The international conference on Advances in Social Network Analysis and Mining (ASONAM 2013) will primarily provide an interdisciplinary venue that will bring together practitioners and researchers from a variety of SNAM fields to promote collaborations and exchange of ideas and practices. ASONAM 2013 is intended to address important aspects with a specific focus on the emerging trends and industry needs associated with social networking analysis and mining. The conference solicits experimental and theoretical works on social network analysis and mining along with their application to real life situations.

Full papers will be reviewed and assessed by the program committee and a "Best Paper Award" ceremony will be organized at the banquet.

Some Computational Challenges in Mining Social Media

Abstract

People of all walks of life use social media for communications and networking. Their active participation in numerous and diverse online activities continually generates massive amounts of social media data. This undoubtedly “big” data presents new challenges to data mining, including how to select salient features for social media data with varied relations, how to assess user vulnerability, and how to ensure that patterns discovered from social media data are valid when no ground truth is available. We will illustrate the intricacies of social media data, present original social-computing problems, deliberate approaches to mining social media data to gain insight from real-world applications and deepen our understanding, and exploit unique characteristics of social media data in developing novel algorithms and computational tools for social media mining.

Short Bio

Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in EECS at Shanghai JiaoTong University. He was recognized for excellence in teaching and research in Computer Science and Engineering at Arizona State University. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating problems that arise in real-world applications with high-dimensional data of disparate forms. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He serves on journal editorial/advisory boards and numerous conference program committees. He is a Fellow of IEEE and a member of several professional societies.

For Industrial Track submission, please send your paper as attachment to Prof. Tansel Özyer tanseletu@gmail.com

Papers which were presented in Istanbul in August 2012 have been included in the digital library. Here is the link: http://www.computer.org/csdl/proceedings/asonam/2012/4799/00/index.html