Community Detection by an Efficient Ant Colony Approach

Andrade, Lúcio Pereira de; Espíndola, Rogério Pinto; Ebecken, Nelson F. F.

doi:10.1007/978-3-319-08326-1_1

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Besides, several studies have been undertaken on ant-based algorithms for community detection in social networks. Lucio et al in [31] proposed a multi-layer ant-based algorithm, which detects communities in networks by locally optimising the modularity using individual ants. Due to the intrinsic multi-level nature of this algorithm, the method potentially can display multi-scale hierarchical structures.…”

Section: Related Workmentioning

confidence: 99%

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

Hosseini

Rezvanian

2020

CAAI trans. intell. technol.

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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.

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Section: Related Workmentioning

confidence: 99%