Conference Information

CSoNet 2023: International Conference on Computational Data and Social Networks

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Submission Date:
2023-09-05
Notification Date:
2023-11-05
Conference Date:
2023-12-11
Location:
Hanoi, Vietnam
Years:
12
Viewed: 22663   Tracked: 1   Attend: 0

Call For Papers

CSoNet 2023 (International Conference on Computational Data and Social Networks) is an academic conference held in Hanoi, Vietnam on 2023-12-11. The paper submission deadline is 2023-09-05. Acceptance notifications are sent on 2023-11-05.

CSoNet 2023 provides a premier interdisciplinary forum to bring together researchers and practitioners from all fields of big data networks, such as billion-scale network computing, data network analysis, mining, security and privacy, and deep learning. CSoNet 2023 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, modeling, and deep learning. The conference solicits theoretical, methodological, empirical, and experimental research reporting original and unpublished results on computational big data networks. The conference will be organized in Hanoi, Vietnam. 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 Networks 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 Energy Efficiency in Mobile Data Networks Natural Language Understanding for Network Analysis E-commerce and Social Media Marketing Deep Learning on Graphs and its Application Stock Market Prediction and Stock Recommendation Anomaly Detection, Security, and Privacy in Big Data Networks Analysis of signed and attributed real-world networks Multidimensional graph analysis Algorithmic fairness in network analysis and graph mining
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