Journal Information

Social Network Analysis and Mining

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Impact Factor:
2.8
Publisher:
Springer
ISSN:
1869-5450
Viewed:
23589
Tracked:
7
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Call For Papers

Social Network Analysis and Mining is an academic journal published by Springer. (ISSN 1869-5450, impact factor 2.8).

Aims and scope 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. The main areas covered by SNAM include: (1) data mining advances on the discovery and analysis of communities, personalization for solitary activities (e.g. search) and social activities (e.g. discovery of potential friends), the analysis of user behavior in open forums (e.g. conventional sites, blogs and forums) and in commercial platforms (e.g. e-auctions), and the associated security and privacy-preservation challenges; (2) social network modeling, construction of scalable and customizable social network infrastructure, identification and discovery of complex, dynamics, growth, and evolution patterns using machine learning and data mining approaches or multi-agent based simulation; (3) social network analysis and mining for open source intelligence and homeland security. Papers should elaborate on data mining and machine learning or related methods, issues associated to data preparation and pattern interpretation, both for conventional data (usage logs, query logs, document collections) and for multimedia data (pictures and their annotations, multi-channel usage data). Topics include but are not limited to: Applications of social network in business engineering, scientific and medical domains, homeland security, terrorism and criminology, fraud detection, public sector, politics, and case studies Anomaly and outlier detection in social networks Behavior and identity detection and monitoring Community discovery in large-scale and complex social networks Contextual social network analysis Data models and query models for social networks and social media Data preparation for social network analysis and mining Data protection inside communities Dynamics and evolution patterns of social networks, trend prediction Evolution of communities in the Web Information acquisition and establishment of social relations Large-scale graph algorithms Link and node prediction in social networks Misbehaviour detection in communities Mobile and stream data analysis for social network applications Multi-agent based social network modeling and analysis Multidisciplinary applications of social network analysis Network integration and conflict resolution Online social networking and human computer interaction Pattern presentation for end-users and experts Personalization for search and for social interaction Recommendations for e-commerce and business applications Recommendation networks Search algorithms on social networks Security and privacy in social networks Social media monitoring and analysis Spatio-temporal aspects in social networks and social media Tools and infrastructures for social networking platforms Web 2.0 and Web communities
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