Social Network Analysis and Mining
Impact Factor & Key Scientometrics

I. Basic Journal Info

Country

Austria

Journal ISSN: 18695450, 18695469

Publisher: Springer-Verlag Wien

History: 2011-2021

Journal Hompage: Link

How to Get Published:

Research Categories

Scope/Description:

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 include1 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. eauctions and the associated security and privacypreservation challenges2 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 multiagent based simulation3 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 multichannel usage data.Topics include but are not limited toApplications of social network in business engineering scientific and medical domains homeland security terrorism and criminology fraud detection public sector politics and case studies.