Community Detection in Social Networks

Ahajjam, Sara; Badir, Hassan

doi:10.1007/978-981-16-3398-0_5

Cited by 4 publications

(1 citation statement)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A review of the latest community detection algorithms can be found in Mester et al. (2021) and Ahajjam and Badir (2022). We can highlight the algorithm introduced by Malhotra and Chug (2021), which uses the Label Propagation Algorithm (LPA) (Largeron et al., 2015), in which the attributes that describe the nodes are adaptable to improve the detection of independent communities even in larger network data sets.…”

Section: Introductionmentioning

confidence: 99%

Random graph generator for leader and community detection in networks

Matos‐Junior

Ospina

Silva

2022

Int Trans Operational Res

View full text Add to dashboard Cite

In complex network analyses, mainly in social networks, the detection of communities is an important source of information for revealing an internal organization of nodes. On the other hand, if the network reveals a leadership structure, it is possible to understand the mechanisms of information dissemination on it. The detection of leaders and communities is a big challenge depending on the complexity level of the network. In the literature, there are some metaheuristic algorithms for detecting leaders and communities based on pattern recognition on the graph associated with the network. In this paper, we developed a random graph system to generate a synthetic network instance with leader and community structures that define a ground truth. We compare this ground truth to determine the performance of the algorithms LCDA 1 and LCDA 2 for detecting leaders and communities. The results corroborate that the benchmarking system would help in selecting useful configurations for practical applications.

show abstract