Novel Community Detection and Ranking Approaches for Social Network Analysis

Abstract

Enterprises are collecting, procuring, storing, and processing increasing quantities of Big Data. This facilitates the detection of new insights that are capable of driving more efficient and effective operations. This provides management with the ability to steer the business proactively. Identifying the crucial nodes, related communities in a network can help in target marketing. Such analyses utilize the concepts of the shortest path, closeness centrality, and clustering coefficient. In this research, we proposed a novel community detection algorithm based on local centrality and node closeness. Exploratory analysis such as the graphical representation of data, to depict an interconnected collection of entities, among people, groups, or products is performed. We also performed network analysis (community detection and ranking algorithms) to analyze the relationships among the entities. The proposed algorithms are applied to multiple datasets to identify the hidden patterns. Among the benchmark datasets, the algorithms were implemented on the American College Football, Dolphin Community, Les Miserables, and Karate Club datasets. We were able to predict the next matches, the most popular member of the club, and their relevant connections with high accuracy as compared to the ground truths. Besides, these algorithms encompass all the features and predict the importance of the community leader, which is a key differentiating factor for the proposed algorithms. Modularity is used as the metric to compare the effectiveness of the proposed methods with state-of-the-art frameworks. The proposed community detection and community ranking algorithms performed well on scale-free networks. We can also identify the hidden patterns of friendships on social media and frequent itemsets purchased together using ranking and community detection methodologies which can help in improving recommendation systems.