A Study on the Effect of 2-Approximate Shortest-Path Integration in Extracting Context-Sensitive Dense Structures

Abstract

Heterogeneous Information Networks (HIN) are introduced to efficiently model real-world systems such as social networks, communication networks, biological networks, etc. Extracting context-sensitive dense structures from HINs provides useful insights to address important problems like community detection, link prediction, etc. Barman et al. [TCSS 2019] proposed k-context technique to extract context-sensitive dense structures from a given HIN. In that algorithm, they used the Floyd-Warshall algorithm to find all-pairs shortest paths in the secondary layer of the HIN. This work studies the impact of replacing the Floyd-Warshall algorithm with a 2-approximate all-pairs shortest-paths algorithm in the k-context technique to extract context-sensitive dense structures from a given HIN. This method improves the theoretical running time of the k-context technique. © 2023 Owner/Author.