Finding overlapping communities based on Markov chain and link clustering

Abstract

Since community structure is an important feature of complex network, the study of community detection has attracted more and more attention in recent years. Despite most researchers focus on identifying disjoint communities, communities in many real networks often overlap. In this paper, we proposed a novel MCLC algorithm to discover overlapping communities, which using random walk on the line graph and attraction intensity. Unlike traditional random walk starting from a node, our random walk starts from a link. First we transform an undirected network graph to a weighted line graph, and then random walks on this line graph can be associated with a Markov chain. By calculating the transition probability of the Markov chain, we obtain the similarity between link pairs. Next the links can be clustered into "link communities" by a linkage method, and these nodes between link communities can be overlapping nodes. When converting the "link communities" into the "node communities", we make a definition of attraction intensity to control the overlapping size. Finally the detected communities are permitted overlapped. Experiments on synthetic networks and some real world networks validate the effectiveness and efficiency of the proposed algorithm. Comparing overlapping modularity Qov with other related algorithms, the results of this algorithm are satisfactory.

Performance Analysis of Clustered LoRa Networks

“…Using the similar approach as obtaining (A.2), we can obtain respectively. Substituting into (D.2) and (D.3) into (D.1), we can obtain (19). The proof of (22) follows on the same procedure as (19) and is hence skipped.…”

Relay Selection Joint Consecutive Packet Routing Scheme to Improve Performance for Wake-Up Radio-Enabled WSNs

“…Since the communication module between nodes is the most energy-consuming part of sensor nodes and the energy consumption of communication operations accounts for 70% of the total energy consumption according to statistics [9,11], it is necessary to reduce energy consumption of nodes and communication operations [9,15]. In order to reduce the energy consumption of communication operations, researchers have also proposed various methods [17,18] and communication protocol is one of the most important parts [19]. A good communication protocol requires small system overhead for communication, high effective communication rate [6,12], low energy consumption [13], low delay [14][15][16][20][21][22], low data collision rate, and high network throughput [23].…”

A Central Edge Selection Based Overlapping Community Detection Algorithm for the Detection of Overlapping Structures in Protein–Protein Interaction Networks

“…Later in 2013, Lan Huang et al proposed an extended link similarity (ELC), considering the similarity among the non-neighbor links [ 24 ]. Recently, an OCD algorithm was proposed based on links by combining a novel link similarity measure with the classic Markov cluster [ 25 ], and Deng X et al proposed another algorithm combining the edge information with the Markov chain to detect overlapping communities [ 26 ]. The algorithms in clustering procedures and overlapping nodes pruning (ONP), such as the nearest neighbor algorithm (NN) [ 27 ] and community detection with an adjustable extent of overlapping [ 28 ], were developed dramatically.…”