Experimental Analysis and Evaluation of Cohesive Subgraph Discovery

TL;DR

This paper systematically evaluates various cohesive subgraph models in social networks through extensive experiments on synthetic and real data, revealing their strengths, limitations, and guiding future model selection.

Contribution

It provides a comprehensive, task-based comparison of cohesive subgraph models, highlighting their performance characteristics and practical applicability.

Findings

Identified key differences in model efficiency and cohesion.

Provided insights into model interpretability and applicability.

Established benchmarks for future cohesive subgraph research.

Abstract

Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or recommendation systems. Despite the introduction of numerous models over the years, a systematic comparison of their performance, especially across varied network configurations, remains unexplored. In this study, we evaluated various cohesive subgraph models using task-based evaluations and conducted extensive experimental studies on both synthetic and real-world networks. Thus, we unveil the characteristics of cohesive subgraph models, highlighting their efficiency and applicability. Our findings not only provide a detailed evaluation of current models but also lay the groundwork for future research by shedding light on the balance between the interpretability and cohesion of the subgraphs. This research guides the selection of suitable models for specific analytical needs and applications, providing valuable insights.