Graph Computing (GC)
Graphs are widely used to model complex data nowadays: social networks, recommendation engines, computer networks, bio-informatics, to name a few. Graph theory has traditionally been a core area of research in computer science and artificial intelligence. With the growing number of applications and amount of data, Graph Computing addresses the storage, analysis, synthesis, and processing of (possibly large) graphs to solve complex problems in industry applications.
TOPICS OF INTEREST include, but are not limited to:
Graph algorithms for industry applications
Fundamentals of graph computing including storage of graphs, graph databases, query languages, query optimization, integrity and security, graph OLAP, graph mining, graph learning, graph reduction, and graph visualization.
Big graph analytics
Integration and platforms
Graph Computing 2022 Co-Chairs
Joseph Barr, Acronis SCS, USA
Gregory Gutin, Royal Holloway, University of London, UK
Peter Shaw, Massey University, New Zealand
Phillip Sheu, University of California, Irvine. USA