2020 - 9th Intl. Conf. on Computational Data and Social Networks (CSoNet 2020)
Topics/Call fo Papers
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 The conference solicits theoretical, methodological, empirical, and experimental research reporting original and unpublished results on computational big data and social networks. 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.
Accepted papers will be published in Springer’s Lecture Notes in Computer Science, and indexed by ISI (CPCI-S, included in ISI Web of Science), EI Engineering Index (Compendex and Inspec databases), ACM Digital Library, DBLP, Google Scholar, MathSciNet, etc. Also, extended versions of selected best papers will be invited for publication in Journal of Combinatorial Optimization, IEEE Transactions on Network Science and Engineering, and Computational Social Networks.
Submission link: https://easychair.org/conferences/?conf=csonet2020
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.
Accepted papers will be published in Springer’s Lecture Notes in Computer Science, and indexed by ISI (CPCI-S, included in ISI Web of Science), EI Engineering Index (Compendex and Inspec databases), ACM Digital Library, DBLP, Google Scholar, MathSciNet, etc. Also, extended versions of selected best papers will be invited for publication in Journal of Combinatorial Optimization, IEEE Transactions on Network Science and Engineering, and Computational Social Networks.
Submission link: https://easychair.org/conferences/?conf=csonet2020
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Last modified: 2020-07-30 16:47:20