AntLP: ant‐based label propagation algorithm for community detection in social networks

Abstract

In social network analysis, community detection is one of the significant tasks to study the structure and characteristics of the networks. In recent years, several intelligent and meta‐heuristic algorithms have been presented for community detection in complex social networks, among them label propagation algorithm (LPA) is one of the fastest algorithms for discovering community structures. However, due to the randomness of the LPA, its performance is not suitable for the general purpose of network analysis. In this study, the authors propose an improved version of the label propagation (called AntLP) algorithm using similarity indices and ant colony optimisation (ACO). The AntLP consists of two steps: in the first step, the algorithm assigns weights for edges of the input network using several similarity indices, and in the second step, the AntLP using ACO tries to propagate labels and optimise modularity measure by grouping similar vertices in each community based on the local similarities among the vertices of the network. In order to study the performance of the AntLP, several experiments are conducted on some well‐known social network datasets. Experimental simulations demonstrated that the AntLP is better than some community detection algorithms for social networks in terms of modularity, normalised mutual information and running time.

An effective and efficient label initialization method based on similarity for community detection in networks

“…Motifs in network are also considered for improving the LPA stability [14]. Also, methods based on genetic algorithms [15] and considering attributes [16] are introduced. Still, the stability and accuracy is a problem unsolved.…”

Cognitive load management in mobile learning systems: principles and theories

“…Some algorithms’ examples dealing with these issues are imputing missing values using linear regression technique (Mostafa 2019 ) to equate the learning mechanism with comparative datasets; the Neural Saliency algorithm guide bi-directional visual perception style transfer (Zhu et al 2020 ) which tries to bring consistency in the way learners perceive imagery. Images are given special attention by learners differently, and this algorithm tries to validate an overall uniform visual perception; AntLP: Ant-based label propagation algorithm for community detection in social networks (Hosseini and Rezvanian 2020 ) which tries to find out the similarities in datasets to provide an optimized community to fill the gap during a learning session; TDD-Net (Ding et al 2019 ) puts forward a tiny defect detection network using the clustering technique to build relationships between hierarchies of events to match a suggestion for the missing information and the Channel-wise attention model-based fire and rating level detection in video (Wu et al 2019 ) which associates artificial intelligence in learning models to detect fire in the surrounding. All these algorithms are aiming to work accurately with activity-based gestures of the individual and associated context and cognitive datasets to provide an automated and blended system.…”

Community Detection Based on Fish School Effect

“…Although the k-means algorithm based on partitioning [16] has the advantage of simplicity, low complexity and fast convergence speed, its distinct method for randomly selecting initial community centers affects community partitioning to a certain extent. The label propagation algorithm (LPA) [17][18] has approximately linear time complexity. On the other hand, outcomes observed from community detection are regularly in volatile.Roy U K proposed a modified local random walk method to catch the fuzzy community based on neighbors' similarity [19].…”