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CSoNet 2020 : 9th International Conference on Computational Data and Social Networks

Dallas, TX
Event Date: Dec 11, 2020 - Dec 13, 2020
Submission Deadline: Aug 15, 2020
Notification of Acceptance: Oct 01, 2020
Camera Ready Version Due : Oct 15, 2020


CSoNet 2020 provides a premier interdisciplinary forum to bring together researchers and practitioners from all fields of big data and social networks, such as billion-scale network computing, social network/media analysis, mining, security and privacy, and deep learning. CSoNet 2020 seeks to address emerging yet important computational problems, with a focus on the fundamental background, theoretical technology development, and real-world applications associated with big data network analysis, modelling, and deep learning. The conference solicits theoretical, methodological, empirical, and experimental research reporting original and unpublished results on computational big data and social networks.

Call For Paper

Topics of interest include, but are not limited to:

  • Real-world Complex Networks Analysis
  • Trends and Pattern Analysis in Social Networks
  • Representation Learning on Networks
  • Big Data Analysis
  • Mathematical Modeling and Analysis of Real-world Social Platforms
  • Network Structure Analysis and Dynamics Optimization
  • Data Network Design and Architecture
  • Information Diffusion Models and Techniques
  • Security and Privacy in Data Networks
  • Efficient Algorithms for Large-scale Data Networks Computing
  • Reputation and Trust in Social Media
  • Social Influence, Recommendation, and Media
  • Applications of Complex Data Network Analysis
  • Energy Efficiency in Mobile Data Networks
  • Natural Language Understanding for Social Media
  • E-commerce and Social Media Marketing
  • Deep Learning on Graphs and its Application
  • Stock Market Prediction and Stock Recommendation with Social Media Data
  • Anomaly Detection, Security, and Privacy in Big Data Networks
  • Analysis of signed and attributed real-world networks
  • Multidimensional graph analysis
  • Algorithmic fairness in social network analysis and graph mining

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