Efficient Approximation of Centrality Measures in Uncertain Graphs

TL;DR

This thesis introduces a novel heuristic algorithm for efficiently approximating centrality measures in uncertain graphs, focusing on harmonic closeness and betweenness centrality, and compares its performance to existing methods.

Contribution

It presents a new heuristic approach for calculating distance-based centrality measures in uncertain graphs, extending previous work with the concept of possible shortest paths.

Findings

Heuristic algorithm outperforms Monte Carlo in runtime and accuracy for harmonic closeness

Proposed method shows efficacy on a variety of graph instances

Algorithms scale well to large graphs

Abstract

In this thesis I propose an algorithm to heuristically calculate different distance measures on uncertain graphs (i.e. graphs where edges only exist with a certain probability) and apply this to the heuristic calculation of harmonic closeness centrality. This approach is mainly based on previous work on the calculation of distance measures by Potamias et al. and on a heuristic algorithm for betweenness centrality by Chenxu Wang and Ziyuan Lin. I extend on their research by using the concept of possible shortest paths, applying them to the afformentioned distances. To the best of my knowledge, this algorithmic approach has never been studied before. I will compare my heuristic results for harmonic closeness against the Monte Carlo method both in runtime and accuracy. Similarly, I will conduct new experiments on the betweenness centrality heuristic proposed y Chenxu Wang and Ziyuan Lin to test its efficacy on a bigger variety of instances. Finally, I will test both of these algorithms on large scale graphs to evaluate the scalability of their runtime.