Communities and Hierarchical Structures in Dynamic Social Networks: Analysis and Visualization

TL;DR

This paper presents a system for analyzing and visualizing dynamic social networks, focusing on detecting community changes and hierarchical structures over time using graph clustering techniques.

Contribution

It introduces a novel system that combines dynamic graph discretization and clustering to reveal community evolution and influential individuals in social networks.

Findings

System effectively detects structural changes in social communities.

Reveals hierarchical roles and influential individuals.

Applied to real datasets showing insightful social structures.

Abstract

Detection of community structures in social networks has attracted lots of attention in the domain of sociology and behavioral sciences. Social networks also exhibit dynamic nature as these networks change continuously with the passage of time. Social networks might also present a hierarchical structure led by individuals that play important roles in a society such as Managers and Decision Makers. Detection and Visualization of these networks changing over time is a challenging problem where communities change as a function of events taking place in the society and the role people play in it. In this paper we address these issues by presenting a system to analyze dynamic social networks. The proposed system is based on dynamic graph discretization and graph clustering. The system allows detection of major structural changes taking place in social communities over time and reveals hierarchies by identifying influential people in a social networks. We use two different data sets for the empirical evaluation and observe that our system helps to discover interesting facts about the social and hierarchical structures present in these social networks.