Social Network Analysis and Mining
| 短名 | Soc. Netw. Anal. Min. |
| Journal Impact | 2.39 |
| 国际分区 | COMPUTER SCIENCE, INFORMATION SYSTEMS(Q3) |
| ISSN | 1869-5450, 1869-5469 |
| h-index | 48 |
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.
期刊主页| 涉及主题 | 计算机科学政治学万维网法学数学人工智能社会化媒体机器学习物理工程类数据科学哲学程序设计语言统计心理学操作系统数据挖掘经济社会学组合数学量子力学生物情报检索社交网络（社会语言学）理论计算机科学社会科学 |
| 出版信息 | 出版商: Springer-Verlag Wien，出版周期: ，期刊类型: journal |
| 基本数据 | 创刊年份: 2011，原创研究文献占比: 91.98%，自引率:8.70%， Gold OA占比: 23.36% |
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