Complexity

Collective Behavior Analysis and Graph Mining in Social Networks


Publishing date
01 Feb 2021
Status
Closed
Submission deadline
09 Oct 2020

Lead Editor

1Beijing Jiaotong University, Beijing, China

2Monash University, Melbourne, Australia

3Northwestern Polytechnical University, Xi’an, China

4University of Reading, Reading, UK

5Beijing Institute of Technology, Beijing, China

This issue is now closed for submissions.
More articles will be published in the near future.

Collective Behavior Analysis and Graph Mining in Social Networks

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Social networks provide a convenient place for people to interact and have taken a significant part in people’s life. An increasing number of social networks emerge and evolve every day, such as online social networks, scientific cooperation networks, airport passage networks, etc. Members in social networks communicate with each other, and they may create new connections or break existing connections, driving the evolution of complex network structure. In addition, dynamics in social networks, such as opinion formation, spreading dynamics and collaborative behaviors, are induced by interpersonal contacts and interactions, and may result in complex collective phenomena, demonstrating the basic role of social networks as a complex system. Analyzing complex human behaviors and mining graph topology can help to understand the essential mechanism of macroscopic phenomena, to discover the potential public interest, and to provide early warnings of collective emergencies. Therefore, social network mining has become a promising research area and attracts lots of attention.

Studies on social networks in general can be divided into two categories, i.e., theoretical modeling and data-driven methods. Theoretical methods use statistical physics, Monte-Carlo simulations and stochastic process, to model human interactions and reveal the microscopic dynamical essence of collective phenomena. However, theoretical methods often lack the ability of practical prediction. Data-driven methods use machine learning, data mining and natural language processing to exploit hidden patterns from the data in social networks, and then estimate the future evolution of social behaviors, but these methods do not have good interpretability of collective phenomena and may have a biased estimation due to uniformly sampling from a whole network. In recent years, big data in social networks also bring challenges to process social data and investigate human behaviors. Therefore, advanced Interdisciplinary data analysis and data mining methods should be proposed and developed to study social networks.

The goal of this Special Issue is to welcome contributions in the quickly growing research field of social networks. We encourage articles with multidisciplinary methods for social data mining. The related disciplines include machine learning, information theory, applied mathematics, computational and statistical physics.

Potential topics include but are not limited to the following:

  • Network representation learning
  • Streaming social data processing
  • Heterogeneous social network mining
  • Deep learning in social computing
  • Behavior analysis on social networks
  • Pattern recognition of behaviors
  • Human sentiment mining and analysis
  • Individual interest modeling
  • Personalized recommender systems
  • Knowledge graph and its applications
  • Essential mechanism of information diffusion and control
  • Modeling the formation and phase transition of collective phenomena
  • Trend prediction of information propagation

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 5323421
  • - Erratum

Erratum to “A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks”

Rongmei Zhao | Xi Xiong | ... | Binyong Li
  • Special Issue
  • - Volume 2021
  • - Article ID 6647664
  • - Research Article

Analysis of the Structural Characteristics of the Online Social Network of Chinese Professional Athletes

Yue Wang | Qian Huang | ... | Bin Zhang
  • Special Issue
  • - Volume 2021
  • - Article ID 2304754
  • - Review Article

Opinion Expression Dynamics in Social Media Chat Groups: An Integrated Quasi-Experimental and Agent-Based Model Approach

Siyuan Ma | Hongzhong Zhang
  • Special Issue
  • - Volume 2021
  • - Article ID 6692210
  • - Review Article

A Review of the Research Progress of Social Network Structure

Ning Li | Qian Huang | ... | Sai-Fu Fung
  • Special Issue
  • - Volume 2021
  • - Article ID 4963903
  • - Research Article

Social Networks through the Prism of Cognition

Radosław Michalski | Boleslaw K. Szymanski | ... | Marcin Kulisiewicz
  • Special Issue
  • - Volume 2020
  • - Article ID 8812459
  • - Research Article

A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data

Dong Zhong | Yi-An Zhu | ... | Jiaxuan He
  • Special Issue
  • - Volume 2020
  • - Article ID 6680954
  • - Research Article

Research on the City Network Structure in the Yellow River Basin in China Based on Two-Way Time Distance Gravity Model and Social Network Analysis Method

Duo Chai | Dong Zhang | ... | Shan Yang
  • Special Issue
  • - Volume 2020
  • - Article ID 6629318
  • - Research Article

The Spread of Information in Virtual Communities

Zhen Zhang | Jin Du | ... | Xiaodan Fan
  • Special Issue
  • - Volume 2020
  • - Article ID 4392975
  • - Research Article

Athlete Social Support Network Modeling Based on Modern Valence Bond Theory

Ningshe Zhao
  • Special Issue
  • - Volume 2020
  • - Article ID 1028941
  • - Research Article

Characterization of 2-Path Signed Network

Deepa Sinha | Deepakshi Sharma
Complexity
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Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.200
Impact Factor2.462
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