Local Edge Betweenness based Label Propagation for Community Detection in Complex Networks

Nowadays, identification and detection community structures in complex networks is an important factor in extracting useful information from networks. Label propagation algorithm with near linear-time complexity is one of the most popular methods for detecting community structures, yet its uncertainty and randomness is a defective factor. Merging LPA with other community detection metrics would improve its accuracy and reduce instability of LPA. Considering this point, in this paper we tried to use edge betweenness centrality to improve LPA performance. On the other hand, calculating edge betweenness centrality is expensive, so as an alternative metric, we try to use local edge betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge Betweenness). Experimental results on both real-world and benchmark networks show that LPA-LEB possesses higher accuracy and stability than LPA when detecting community structures in networks.Comment: 6 page

A Faster Algorithm for the Limited-Capacity Many-to-Many Point Matching in One Dimension

Given two point sets S and T on a line, we present the first linear time algorithm for finding the limited capacity many-to-many matching (LCMM) between S and T improving the previous best known quadratic time algorithm. The aim of the LCMM is to match each point of S (T) to at least one point of T (S) such that the matching costs is minimized and the number of the points matched to each point is limited to a given number.Comment: 18 pages, 7 figures. arXiv admin note: text overlap with arXiv:1702.0108