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For more than a century, social networks have been studied in a variety of disciplines including sociology, anthropology, psychology, and economics. The Internet, the social Web, and other large-scale, socio-technological infrastructures have triggered a growing interest and significant methodological advancements in social network analysis and mining. Method development in graph theory, graph algorithms, statistics, data mining and machine learning, and statistical mechanics is inspired by new research problems and, in turn, opens up further possibilities for application. These spiraling trends have led to a rising prominence of social network analysis and mining methods and tools in academia, politics, security, and business.
The international conference series on Advances in Social Network Analysis and Mining (ASONAM) provides an interdisciplinary venue that brings together researchers and practitioners from a broad variety of fields to promote collaborations and exchange of ideas and practices. ASONAM is intended to address important aspects with a specific focus on emerging trends and industry needs. The conference solicits empirical, experimental, methodological, and theoretical research reporting original and unpublished results on social network analysis and mining along with applications. More specialized topics within ASONAM 2019 include, but are not limited to:
Techniques
Data collection and quality
Big data and scalability
Deep learning and embeddings
Statistical learning
Algorithms and techniques
Visualization
Modeling and simulation
Problems
Centrality and roles
Community detection
Link prediction
Information diffusion
Influence propagation
Anomaly detection
Network macro structures
Network evolution
Emergence
Privacy and security
Collective behavior
Crowd sourcing
Social Recommender Systems
Misinformation and Misbehavior Analysis and Detection
Reputation and Trust in Social Networks
Fairness Bias and Transparency in Social Media
Application domains
Online and offline social networks
Multirelational, multidimensional, multi-aspect, multilayer networks
Time-evolving networks
Probabilistic networks
Semantic networks
Social geography and spatial networks
Social, cultural, and cyber anthropology
Policy impact and analysis
Networks in biology and ecology
Digital Humanities
To fully embrace the fast-growing and vigorously dynamic trend of social network approaches and applications, ASONAM 2019 is eager to consider any breakthroughs in social network analysis and mining in the broadest possible sense.
General areas of interest to ASONAM 2019 include information science and mathematics, communication studies, business and organizational studies, sociology, psychology, anthropology, applied linguistics, biology and medicine.
Submission
The papers will be reviewed and assessed by the program committee. Full paper manuscripts must be in English with a length of 8 pages using the IEEE two-column template. We solicit also short paper with a maximum length of 4 pages. Submissions should include the title, author(s), affiliation(s), e-mail address(es), and abstract on the first page.
EasyChair submission link: https://easychair.org/conferences/?conf=asonam2019
Papers will be accepted for the conference based on the reviewers' comments on their originality, timeliness, significance, relevance, and clarity of presentation. A Best Paper Award ceremony will be organized at the banquet. If the paper is accepted, the paper will appear in the proceedings of the conference if one author presents the paper at the conference and at least one author registers as a full conference participant.
Publications
Accepted and presented papers will be included in the Conference Proceedings and forwarded for inclusion in IEEE Computer Society Digital Library (CSDL), IEEE eXplore, and the ACM Digital Library. The conference proceedings will be submitted for El indexing through INSPEC by the IEEE. Proceedings will be included in several indexes, e.g., Web of Science, SCOPUS, etc.
If you have any questions on ASONAM 2019, please send email to asonam2019 (AT) gmail (dot) com.
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