Detecting Disjoint Communities in a Social Network based on the Degrees of Association between Edges and Influential Nodes

Abstract

Detecting communities is crucial to understanding the dynamics of their members. However, the detection of "good" communities is deemed demonstrably problematic, which is mainly due to the following two factors. First, real-world networks are complex and require optimizing multi-objective functions for capturing their community structures, whereas most current approaches optimize only one or two objective functions. Second, most current approaches detect communities in respect of the independence of how closely associated their connections are based on the global relative influences of the edges connecting them. To overcome these limitations, a clustering method needs to optimize multi-objective functions and employ global preprocessing techniques that consider the topology of the entire network. We, therefore, proposed a system called DAVE, which optimizes four objective functions that capture the community structures in most real-word network settings, and detects communities with regards of how closely associated their connections are based on the relative influences of the edges connecting them. We proposed novel formulas that capture these functions. Our method is the first to utilize the prediction of node-node associations based on global node-edge degrees of association. After ranking nodes based on their global relative influences on the network, some of the top-ranked ones will serve as core seeds for constructing communities. Then, the degrees of association between influential edges and seed nodes are computed. DAVE assigns a node to a community, only if each edge in the shortest path from this node to the community's core seed node is both influential and has significant degree of association with the core node. We evaluated DAVE by comparing it empirically and experimentally with sixteen methods. Results showed a remarkable improvement.

Social Theory Aware Influence Maximization Approach for Target Community Detection

“…Representative results include: Literature. [16] studied the evaluation method of microblog community influence, proposed an index system based on microblog information dissemination mechanism, combined quantitative indicators and qualitative indicators, and used principal component analysis to connect these the indicators combined into several comprehensive hands, which simplifies the indicator system; Literature [17] proposed a community impact evaluation model framework from the perspective of information dissemination and a set of related definitions of community impact evaluation forms, involving user impact and community impact; Literature [18] proposed a variable influence community detection method based on PageRank, which can adjust the community where a specific node is located and increase its influence. However, for the target community discovery task, in addition to accurately mining the community composed of high-quality nodes similar to the sample nodes given by the user, the ability of the community to spread the internal information of the community to external users, that is, the external influence of the community is also a significant factor.…”

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

“…Taha [148] proposed a method that detects communities and their influential nodes by optimizing, among others, the partitions separability of communities. The method does so by selecting well-distributed core nodes.…”

Detecting implicit cross-communities to which an active user belongs

“…That is, we quantify the extent to which the cross-members shared by two SACs relate the two SACs. The quantification is expressed in terms of a score that serves as an indicator of the local influence of the Association Edge [ 24 ]. The BI score of an Association Edge reflects the edge’s chance of passing information between the two SACs at its end points relative to the other Association Edges connecting the same two SACs.…”