Dear Colleagues,

With the development of Internet technology, social media have demonstrated their strength with regard to generating complex structure and producing group phenomena, and social networks exhibit new features, such as symmetry, heterogeneity, chaos, nonlinearity, etc. Symmetry and asymmetry both exist in the topological structure and collective user behaviors of online social networks. For instance, in Facebook, user relationships are reciprocal, while on twitter, connections between users are directed. The actions of users are nonlinear and chaotic, and local behaviors may arouse macroscopic response and lead to online emergence. It has been proven that the above features of social networks play a significant role in the formation of collective structure and phenomena, and analyzing and mining the essential features can help studies on information diffusion, social recommendation, community detection, etc. Multidisciplinary theory and methods can be used to explore social networks, including statistical physics, system engineering, data mining, and natural language processing. Theoretical methods in statistical physics have the advantage of revealing microscopic mechanisms and interpreting macroscopic phenomena, while data-driven methods in machine learning are able to discover hidden patterns from social data and predict unknown trends. The integration of both theoretical and data-driven methods is a promising direction for research on social networks.

This Special Issue aims to highlight and advance contributions in the quickly growing research field of complex structure and behaviors in social networks. We encourage both original research and reviews of research with multidisciplinary methods for social data mining. Research on the symmetry and asymmetry in other complex interactive networks, such as scientific cooperation networks, are also welcomed.

Dr. Fei Xiong
Dr. Shirui Pan
Dr. Hongshu Chen
Dr. Li Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• network dynamic modeling
• dynamic community detection
• network representation learning
• social user identification and mining
• human sentiment classification
• personalized recommender systems
• knowledge graph and its applications
• information diffusion and control
• collective phenomena analysis
• transfer learning across heterogeneous social networks