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.

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Keynote Speakers

Some Computational Challenges in Mining Social Media

  • Huan LiuData Mining and Machine Learning Lab, Arizona State University, Tempe, AZ, USA


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.

Unraveling daily human mobility motifs

  • Marta GonzalezMIT, Cambridge, MA, USA


Time scales differentiate human mobility. While the mechanism for longtime scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analyzing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, an analytically tractable framework for Markov chains can reproduce daily human mobility by modeling periods of high-frequency trips followed by periods of lower activity as the key ingredient.

Short Bio

Gilbert Winslow Career Development Assistant Professor of Civil and Environmental Engineering, MIT joint with Engineering Systems (ESD) and the Operations Research Center (ORC). Before joining MIT, she was research associate at the Center for Complex Network Research (Barabási Lab) at Northeastern University, where she joined right after her PhD at Stuttgart Universität, Germany, in 2006.

Marta’s research interests and direction are mainly aimed at advancing the understanding of the laws and principles that characterize human behavior and result in collective social phenomena. More specifically her work falls into the category of complex networks and statistical physics applied to social dynamic systems. Current research explores human mobility patterns using mobile phone communication; data mining combined with geographic information systems (GIS), and urban transportation models (see details at: http://web.mit.edu/humnet/index.shtml

Large Graph Mining - Patterns, explanations, and cascade analysis

  • Christos FaloutsosCarnegie Mellon University, Pittsburgh, PA, USA


What do graphs look like? How do they evolve over time? How does influence/news/viruses propagate, over time? We present a long list of static and temporal laws, and some recent observations on real graphs. We show that fractals and self-similarity can explain several of the observed patterns, and we conclude with cascade analysis and a surprising result on virus propagation and immunization.

Short Bio

Christos Faloutsos is a Professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award in ICDM 2006, the SIGKDD Innovations Award (2010), nineteen "best paper" awards (including two "test of time" awards), and four teaching awards. He is an ACM Fellow, he has served as a member of the executive committee of SIGKDD; he has published over 200 refereed articles, 11 book chapters and one monograph. He holds six patents and he has given over 35 tutorials and over 15 invited distinguished lectures. His research interests include data mining for graphs and streams, fractals, database performance, and indexing for multimedia and bio-informatics data.

Scientific Topics

  • Anomaly detection in social network evolution
  • Application of social network analysis
  • Application of social network mining
  • Communities discovery and analysis in large scale online social networks
  • Communities discovery and analysis in large scale offline social networks
  • Connection between biological similarities and social network formulation
  • Contextual social network analysis
  • Crime data mining and network analysis
  • Cyber anthropology
  • Dark Web
  • Data protection inside communities
  • Detection of communities by document analysis
  • Economical impact of social network discovery
  • More...

Previous Conferences


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For Industrial Track submission, full paper submission deadline is May 31 2013 and you may send your paper as attachment to Prof. Tansel Özyer

For Poster Slides submission, send your paper as attachment to Omar Zarour or Omar Addam

For presentations

  • Full paper is 30 minutes
  • Short paper is 20 minutes
  • Poster paper is 3 minutes
  • Full and short presentations include a 3-5 minute question/answers period
  • No questions/answers for posters, they should be done during the actual posters sessions during the reception on 25 and on 26-27 August

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


  • IEEE Computer Society
  • ACM