Shortest Path Analysis in Social Graphs

TL;DR

This paper empirically analyzes how shortest path algorithms behave on social networks by examining traversal patterns and vertex frequencies, revealing insights into network properties like degree distribution and clustering.

Contribution

It introduces an empirical approach combining shortest path computation and pattern mining to analyze traversal behaviors in social graphs.

Findings

Vertices frequently occur in shortest paths depending on network properties

Traversal patterns correlate with degree distribution and clustering coefficient

Provides insights into the structure of social networks through path analysis

Abstract

The shortest path problem is among the most fundamental combinatorial optimization problems to answer reachability queries. It is hard to deter-mine which vertices or edges are visited during shortest path traversals. In this paper, we provide an empirical analysis on how traversal algorithms behave on social networks. First, we compute the shortest paths between set of vertices. Each shortest path is considered as one transaction. Second, we utilize the pat-tern mining approach to identify the frequency of occurrence of the vertices. We evaluate the results in terms of network properties, i.e. degree distribution, clustering coefficient.