Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach

The booming of Social Internet Things (SIoT) has witnessed the significance graph mining and analysis for social network management. Online Networks (OSNs) can be efficiently managed by monitoring users’ behaviors within a cohesive group represented maximal clique. They further provide valued intelligence their users. Maximal Cliques Problem (MCP) as fundamental problem in is to identify cliques graph. Existing studies on MCP mainly focus static graphs, with less attention detection dynamic evolution OSNs. To fill this gap, we adopt Formal Concept Analysis (FCA) theory represent analyze networks. We then develop two novel formal concepts generation algorithms, termed Add-FCA Dec-FCA, that applicable OSNs detecting characterizing process Extensive experimental results are conducted investigate demonstrate correctness effectiveness proposed algorithms. reveal our algorithms capture manage evolutionary patterns cliques, including unchanged, changed, added, vanished OSNs, quantitative relation among them presented. In addition, an illustrative example presented verify usefulness approach.