Abstract

Graphs are mathematical models to represent relationships, and graph theories have an important role in recent research in the computer science area. These days, there are many kinds of graph-structured data such as social network service and biological and location data. And the graph data are now easily considered big data. Analyzing such graph data is an important problem. In this paper, we apply four major centralities and PageRank algorithms to real-world undirected graph data and find some empirical relationships and features of the algorithms. The results can be the starting point of many data-driven and theoretical link-based graph studies as well as social network service analysis.

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