1,992 research outputs found
A Faster Method to Estimate Closeness Centrality Ranking
Closeness centrality is one way of measuring how central a node is in the
given network. The closeness centrality measure assigns a centrality value to
each node based on its accessibility to the whole network. In real life
applications, we are mainly interested in ranking nodes based on their
centrality values. The classical method to compute the rank of a node first
computes the closeness centrality of all nodes and then compares them to get
its rank. Its time complexity is , where represents total
number of nodes, and represents total number of edges in the network. In
the present work, we propose a heuristic method to fast estimate the closeness
rank of a node in time complexity, where . We
also propose an extended improved method using uniform sampling technique. This
method better estimates the rank and it has the time complexity , where . This is an excellent improvement over the
classical centrality ranking method. The efficiency of the proposed methods is
verified on real world scale-free social networks using absolute and weighted
error functions
Incremental closeness centrality in distributed memory
Networks are commonly used to model traffic patterns, social interactions, or web pages. The vertices in a network do not possess the same characteristics: some vertices are naturally more connected and some vertices can be more important. Closeness centrality (CC) is a global metric that quantifies how important is a given vertex in the network. When the network is dynamic and keeps changing, the relative importance of the vertices also changes. The best known algorithm to compute the CC scores makes it impractical to recompute them from scratch after each modification. In this paper, we propose Streamer, a distributed memory framework for incrementally maintaining the closeness centrality scores of a network upon changes. It leverages pipelined, replicated parallelism, and SpMM-based BFSs, and it takes NUMA effects into account. It makes maintaining the Closeness Centrality values of real-life networks with millions of interactions significantly faster and obtains almost linear speedups on a 64 nodes 8 threads/node cluster
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