Optimization Techniques for Multiple Centrality Computations

Abstract

A broad range of data has a graph structure, such as the Web link structure, online social networks, or online communities whose members rate each other (reputation systems) or rate items (recommender systems). In these contexts, a common task is to identify important vertices in the graph, e.g., influential users in a social network or trustworthy users in a reputation system, by means of centrality measures. In such scenarios, several centrality computations take place at the same time, as we will explain. With centrality computation being expensive, performance is crucial. While optimization techniques for single centrality computations exist, little attention so far has gone into the computation of several centrality measures in combination. In this paper, we investigate how to efficiently compute several centrality measures at a time. We propose two new optimization techniques and demonstrate their usefulness both theoretically as well as experimentally on synthetic and on real-world data sets.KeywordsReputation SystemExchange MeasurementsLoop FusionSubset RelationshipIncidence TableThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.