Community Detection in Signed Social Networks Using Multiobjective Genetic Algorithm

Abstract

Clustering of like‐minded users is basically the goal of community detection (CD) in social networks and many researchers have proposed different algorithms for the same. In signed social networks (SSNs) where type of link is also considered besides the links itself, CD aims to partition the network in such a way to have less positive inter‐connections and less negative intra‐connections among communities. So, approaches used for CD in unsigned networks do not perform well when directly applied on signed networks. Most of the CD algorithms are based on single objective optimization criteria of optimizing modularity which focuses only on link density without considering the type of links existing in the network. In this work, a multiobjective approach for CD in SSNs is proposed considering both the link density as well as the sign of links. Precisely we are developing a method using modularity, frustration and social balance factor as multiple objectives to be optimized (M‐F‐SBF model). NSGA‐II algorithm is used to maintain elitism and diversity in the solutions. Experiments are performed on both existing benchmarked and real‐world datasets show that our approach has led to better solutions, clearly indicating the effectiveness of our proposed M‐F‐SBF model.

Modularity, balance, and frustration in student social networks: The role of negative relationships in communities

“…However, this is not so simple, just removing nodes from a community or splitting a community into two causes the modularity function to decrease. Recent studies have addressed this problem through genetic and evolutive algorithms [ 18 ].…”

“…For example, states in a triad (three connected nodes), in which the relations of friend-enemy tend to converge to two balanced states: ‘the friend of my friend is my friend’ and ‘the enemy of my enemy is my friend’, and otherwise there will be tension among them [ 15 ]. Research based on this theory has been carried out to adress very important issues, such as exchanges of opinion [ 16 ], social influence [ 17 ], social balance in signed networks, and social balance in signed communities [ 18 ]. On the other hand, in signed communities research, frustration is a concept that indicates how far away is a partition of communities from the definition.…”

Enhance Arabic Clustering using an Educated Text Stemmer with WordNet as Extraction and Water Cycle Cluster

“…This allows the potential for obtaining good initial clustering centroids, improving the refinement of the clustering centroids, and determining the optimal clustering centres [39]. The utilisation of nature-inspired metaheuristic algorithms is a widely-adopted practice in multiple sectors, which include computer science [40], data mining [41], industry [42], agriculture [43], computer vision [44], forecasting [45], medicine and biology [46], scheduling [47], economy [48], and engineering [49]. In this work, it was used as an Arabic optimisation cluster.…”