Optimizing Multiple Centrality Computations for Reputation Systems

摘要

In open environments, deciding if an individual is trustworthy, based on his past behavior, is fundamentally important. To accomplish this, centrality in a so-called feedback graph is often used as a frustmeasure. The nodes of this graph represent the individuals, and an edge represents feedback that evaluates a past interaction. In the open environments envisioned where individuals can specify for themselves of how to derive their trust in others, we observe that several centrality computations take place at the same time. With' centrality computation being an expensive operation, performance is an important issue. While techniques for the optimization of a single centrality computation exist, little attention so far has gone into the computation of several centrality measures in combination. In this paper, we investigate how to compute several centrality measures at the same time efficiently. We propose two new optimization techniques and demonstrate their usefulness experimentally both on synthetic and on real-world data sets.