Graph Theoretic Approaches for Analyzing Large-Scale Social Networks

Noting that social network analysis falls within the realm of big data computing, computer and information scientists synthesize recent research in using graph theory for analyzing large-scale social networks. Their topics include a dynamic and context-aware social network approach for multiple criteria decision making through graph-based knowledge learning, undirected bipartite networks as an alternative methodology to probabilistic explores: online interaction and academic attainment in massive open online courses, parallelizing large-scale graph algorithms using the Apache Spark distributed memory system, towards a unified semantic model for online social networks to ensure interoperability and aggregation for analysis, and whether to trust online health information: identifying influential nodes. Annotation ©2017 Ringgold, Inc., Portland, OR (protoview.com)