Social Network Analysis and Mining (SNAM) is a multidisciplinary journal serving researchers and practitioners in academia and industry. It is the main venue for a wide range of researchers and readers from computer science, network science, social sciences, mathematical sciences, medical and biological sciences, financial, management and political sciences. We solicit experimental and theoretical work on social network analysis and mining using a wide range of techniques from social sciences, mathematics, statistics, physics, network science and computer science.
Read, download and save these articles online. They are available online for free until April 15, 2013:
- Social networks and online environments: when science and practice co-evolve - Devan Rosen, George A. Barnett, Jang Hyun Kim
- Social network analysis: developments, advances, and prospects - John Scott
- Supervised methods for multi-relational link prediction - Darcy Davis, Ryan Lichtenwalter, Nitesh V. Chawla
- Trends in science networks: understanding structures and statistics of scientific networks - Miray Kas, Kathleen M. Carley, L. Richard Carley
- Modeling blogger influence in a community - Nitin Agarwal, Huan Liu, Lei Tang, Philip S. Yu
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Social Network Analysis and Mining (SNAM) is intended to be a multidisciplinary journal to serve both academia and industry as a main venue for a wide range of researchers and readers from computer science, social sciences, mathematical sciences, medical and biological sciences. The main areas covered by SNAM include: (1) data mining advances on the discovery and analysis of communities, personalization for solitary activities (like search) and social activities (like discovery of potential friends), the analysis of user behavior in open forums (like conventional sites, blogs and forums) and in commercial platforms (like e-auctions), and the associated security and privacy-preservation challenges; (2) social network modeling, construction of scalable, customizable social network infrastructure, identification and discovery of dynamics, growth, and evolution patterns using machine learning approaches or multi-agent based simulation.
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