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

Abstract

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

A Comprehensive Survey on Security, Trust Management and Privacy Preservation for Social-Internet-of-Things (S-IoT)

“…To minimize the success rate author considered optimized coding parameters, repair internal, selecting new caching objects (nodes) to repair the lost data, selecting Context-Helper (CH) for context requester and allocation of CH to new caching nodes. Yang et al (2021) to use the Formal Concept Analysis (FCA) theory to effectively identify maximum cliques in Online Social Networks (OSN) and control their growth. However, the properties of dynamic changes in maximum cliques in OSNs have not been taken into account.…”

“…However, the properties of dynamic changes in maximum cliques in OSNs have not been taken into account. To aid clique discovery, (Yang et al, 2021) proposed two calculations: Add-FCA and Dec-FCA. The proposed algorithms can promptly and accurately identify the four categories of maximum cliques-namely, unaltered, modified, added and disappeared maximal cliques (Yang et al, 2021).…”

Skyline (λ, k)-Cliques Identification From Fuzzy Attributed Social Networks

“…A cohesive subgraph is a primary vehicle for social networking analysis. There have been a large number of cohesive subgraph models, such as k-cliques [20], [21], kclique community [20], maximal cliques [22], k-core [23], k-truss [24], and social-balanced densest subgraph [25] are emerging from complex networks/social networks. However, these models aim to process the topological structure of social networks only, and neglect the attributes of nodes.…”

Incremental construction of three-way concept lattice for knowledge discovery in social networks

“…Interestingly, each OE concept can be obtained by exchanging the extent and intent of a certain AE concept. For example, for an AE concept ( (2,5,6), (1,4,7,8), (2,5,6)) (♯12 AE concept), we can easily exchange the order of extent and intent, then an OE concept ( (2,5,6), (2,5,6), (1,4,7,8)) (♯20 OE concept) can be obtained.…”

“…recommendation systems [2], virtual machines scheduling [3], social networks analysis [4,5] and social internet of things management [6]. However, a critical and common task for achieving the above services is to generate concept lattice efficiently.…”