2016 Ieee Acm International Conference On Advances In Social Networks Analysis And Mining Asonam

The conference will consider papers in all areas of social networks and mining 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 2015) 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 2015 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

The conference solicits experimental and theoretical works on social network analysis and mining along with their application to real life situations

The conference solicits experimental and theoretical works on social network analysis and mining along with their application to real life situations

Social network analysis and mining

ASONAM '15: Advances in Social Networks Analysis and Mining 2015 Aug 25, 2015-Aug 28, 2015 Paris, France. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

The contributors in this book share, exchange, and develop new concepts, ideas, principles, and methodologies in order to advance and deepen our understanding of social networks in the new generation of Information and Communication Technologies (ICT) enabled by Web 2.0, also referred to as social media, to help policy-making. This interdisciplinary work provides a platform for researchers, practitioners, and graduate students from sociology, behavioral science, computer science, psychology, cultural studies, information systems, operations research and communication to share, exchange, learn, and develop new concepts, ideas, principles, and methodologies. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining will be of interest to researchers, practitioners, and graduate students from the various disciplines listed above. The text facilitates the dissemination of investigations of the dynamics and structure of web based social networks. The book can be used as a reference text for advanced courses on Social Network Analysis, Sociology, Communication, Organization Theory, Cyber-anthropology, Cyber-diplomacy, and Information Technology and Justice.

This book focusses on recommendation, behavior, and anomaly, among of social media analysis. First, recommendation is vital for a variety of applications to narrow down the search space and to better guide people towards educated and personalized alternatives. In this context, the book covers supporting students, food venue, friend and paper recommendation to demonstrate the power of social media data analysis. Secondly, this book treats behavior analysis and understanding as important for a variety of applications, including inspiring behavior from discussion platforms, determining user choices, detecting following patterns, crowd behavior modeling for emergency evacuation, tracking community structure, etc. Third, fraud and anomaly detection have been well tackled based on social media analysis. This has is illustrated in this book by identifying anomalous nodes in a network, chasing undetected fraud processes, discovering hidden knowledge, detecting clickbait, etc. With this wide coverage, the book forms a good source for practitioners and researchers, including instructors and students.