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Computational Social Networks - Springer | 2023 Cite Score:10.9|

Computational Social Networks Journal With Cite Score

Cite Score and Journal Rank of Computational Social Networks

  • About: The Computational Social Networks Journal is a peer-reviewed publication dedicated to the study of social networks using computational methods. It serves as a platform for researchers, data scientists, and practitioners to publish original research articles, reviews, and technical notes on the application of computational techniques to analyze, model, and understand social networks.
  • Objective:
    The primary objective of the journal is to advance the field of social network analysis by leveraging computational methods. It aims to facilitate the dissemination of research that applies data-driven and algorithmic approaches to study the structure, dynamics, and behavior of social networks.
  • Topics Covered:
    The Computational Social Networks Journal covers a wide range of topics including: Algorithms and models for social network analysis Network visualization and data representation Computational methods for network dynamics and evolution Big data and machine learning techniques in social networks Social influence and behavior modeling Network-based prediction and recommendation systems Privacy, security, and ethical issues in social network analysis Applications of social network analysis in various domains, such as marketing, epidemiology, and sociology
  • Impact:
    The journal significantly impacts both academia and industry by promoting research that enhances the understanding and application of computational techniques in social network analysis. Its publications contribute to the development of innovative tools and methodologies for analyzing and interpreting social network data, leading to more informed insights and decision-making.
  • Significance:
    The Computational Social Networks Journal is significant in advancing the field of social network analysis by providing a forum for high-quality, peer-reviewed research. By focusing on computational approaches, the journal supports the development of new techniques and applications that drive progress in understanding social networks and their impact on various aspects of society.

  • Editor-in-Chief:  Ding-Zhu Du

  • Scope: Computational Social Networks is a journal that focuses on the analysis and modeling of social networks using computational methods. It addresses how computational techniques can be applied to understand, analyze, and predict social behavior and interactions. Here are the key areas typically covered in this journal:
  • 1. Social Network Analysis
    Research on methods and algorithms for analyzing social networks
    Studies on network metrics, community detection, and structural analysis
    Applications of social network analysis in various domains such as sociology, economics, and epidemiology
  • 2. Computational Models of Social Networks
    Development and application of computational models to simulate social networks and dynamics
    Studies on agent-based models, network evolution models, and simulation techniques
    Analysis of model performance and validation against real-world data
  • 3. Social Network Data Mining
    Research on techniques for mining and extracting useful information from social network data
    Studies on pattern recognition, anomaly detection, and trend analysis in social networks
    Applications of data mining techniques to understand social phenomena and behaviors
  • 4. Machine Learning for Social Networks
    Application of machine learning algorithms to social network analysis and prediction
    Studies on supervised and unsupervised learning methods for network classification, clustering, and recommendation
    Development of novel machine learning techniques tailored for social network data
  • 5. Social Media Analytics
    Research on analyzing and interpreting data from social media platforms
    Studies on sentiment analysis, influence measurement, and user behavior modeling
    Applications of social media analytics in marketing, public relations, and social research
  • 6. Network Dynamics and Evolution
    Research on the dynamics and evolution of social networks over time
    Studies on the impact of events, trends, and interactions on network structure and behavior
    Analysis of longitudinal data to understand changes in network topology and relationships
  • 7. Privacy and Security in Social Networks
    Research on privacy concerns and security issues related to social network data
    Studies on data protection, access control, and anonymization techniques
    Analysis of the impact of privacy policies and security measures on user behavior and data integrity
  • 8. Social Network Visualization
    Development of techniques and tools for visualizing social network data
    Studies on visualization methods for exploring network structure, relationships, and dynamics
    Applications of visualization in understanding complex social systems and facilitating data interpretation
  • 9. Influence and Information Propagation
    Research on the mechanisms of influence and information dissemination in social networks
    Studies on viral marketing, rumor spreading, and opinion dynamics
    Analysis of factors affecting the spread of information and influence in different types of networks
  • 10. Interdisciplinary Approaches
    Research on integrating insights from social sciences, computer science, and other disciplines to study social networks
    Studies on collaborative approaches to understanding social phenomena and network behavior
    Analysis of interdisciplinary methods and their impact on social network research
  • Latest Research Topics for PhD in Social Networks

  • Print ISSN:  21974314

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  • Abstracting and Indexing:  Scopus

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  • Subject Area and Category:   Computer Science, Computer Science Applications, Human-Computer Interaction, Information Systems, Mathematics, Modeling and Simulation

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  • Cite Score:  10.9

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  • Journal Rank(SJR):  0.518