Affiliation:
1. Harbin Institute of Technology, China
Abstract
Centrality is a family of metrics for characterizing the importance of a vertex in a graph. Although a large number of centrality metrics have been proposed, a majority of them ignores uncertainty in graph data. In this article, we formulate closeness centrality on uncertain graphs and define the batch closeness centrality evaluation problem that computes the closeness centrality of a subset of vertices in an uncertain graph. We develop three algorithms,
MS-BCC
,
MG-BCC,
and
MGMS-BCC
, based on sampling to approximate the closeness centrality of the specified vertices. All these algorithms require to perform breadth-first searches (BFS) starting from the specified vertices on a large number of sampled possible worlds of the uncertain graph. To improve the efficiency of the algorithms, we exploit operation-level parallelism of the BFS traversals and simultaneously execute the shared sequences of operations in the breadth-first searches. Parallelization is realized at different levels in these algorithms. The experimental results show that the proposed algorithms can efficiently and accurately approximate the closeness centrality of the given vertices.
MGMS-BCC
is faster than both
MS-BCC
and
MG-BCC
because it avoids more repeated executions of the shared operation sequences in the BFS traversals.
Funder
National Natural Science Foundation of China
Open Research Projects of Zhejiang Lab
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications