Efficient Top-k Ego-Betweenness Search

TL;DR

This paper introduces efficient algorithms for finding the top-k vertices with the highest ego-betweenness in large graphs, including dynamic updates and parallel processing, validated by extensive experiments.

Contribution

It presents novel search algorithms with upper bounds, update strategies, and parallelization techniques for ego-betweenness top-k queries, advancing scalability and efficiency.

Findings

Algorithms outperform baseline methods in speed and accuracy.

Proposed methods scale well to large real-world datasets.

Dynamic and parallel algorithms effectively handle graph updates.

Abstract

Betweenness centrality, measured by the number of times a vertex occurs on all shortest paths of a graph, has been recognized as a key indicator for the importance of a vertex in the network. However, the betweenness of a vertex is often very hard to compute because it needs to explore all the shortest paths between the other vertices. Recently, a relaxed concept called ego-betweenness was introduced which focuses on computing the betweenness of a vertex in its ego network. In this work, we study a problem of finding the top-k vertices with the highest ego-betweennesses. We first develop two novel search algorithms equipped with a basic upper bound and a dynamic upper bound to efficiently solve this problem. Then, we propose local-update and lazy-update solutions to maintain the ego-betweennesses for all vertices and the top-k results when the graph is updated, respectively. In addition, we also present two efficient parallel algorithms to further improve the efficiency. The results of extensive experiments on five large real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms.