|
| Authors and year | Approach | Main findings |
|
| Li et al. (2019) [17] | Page rank centrality-based | This work shows that the proposed methodology improves the accuracy and efficiency of seed-expansion community detection methods |
| Gao et al. (2021) [18] | Page rank centrality-based | The authors propose a constrained personalized PageRank algorithm for detecting overlapping communities, which involves assigning each node a probability distribution over the communities and updating the distribution through iterations |
| Huang et al. (2022) [19] | Leader rank centrality-based | The authors also compare the results of their method with those of other centrality measures, such as degree centrality and eigenvector centrality, and find that their method outperforms these traditional measures in identifying influential nodes |
| Kiruthika et al. (2022) [20] | Eccentricity centrality-based | The authors describe that the performance of the algorithms varies depending on the characteristics of the network and that no single algorithm is universally best |
| Yang et al. (2018) [21] | Maximizing influence of nearby nodes | Development of a more efficient algorithm for community detection |
| Ma et al. (2019) [22] | Identification of cliques | This approach allows the identification of nodes that overlap in multiple communities |
| Zhang et al. (2021) [23] | Multiobjective optimization | Development of an efficient method for finding sets of nodes that form communities in large bipartite graphs, where each node has multiple attributes |
| Wang et al. (2021) [24] | Identification of dense subgraphs | Development of a new algorithm for community detection in large-scale networks |
| El Kouni (2020) [25] | Node importance | This work suggests that incorporating node importance measures into the labeling process can significantly improve the performance of label propagation-based algorithms for overlapping community detection in complex networks |
| Hesamipour et al. (2022) [26] | Multiobjective optimization on the basis of modularity, heterogeneity, and minimization of communities | The findings of this work have important implications for improving the accuracy and efficiency of community detection algorithms, particularly in cases where the network is large and complex |
| Yang et al. (2022) [27] | Multiobjective optimization on the basis of network structure | This work suggests that incorporating node classification information into community detection algorithms can significantly improve their performance and that MOEAs can be a useful approach for tackling the challenges of community detection in complex networks |
| Su et al. (2022) [28] | Convolutional neural networks | The main findings of the survey suggest that deep learning-based approaches have the potential to significantly improve community detection performance, but there are still much research studies to be conducted to fully realize this potential |
| Choong et al. (2020) [29] | Deep learning and multiobjective optimization | The authors suggest that the proposed dual optimization strategy for VGAE can significantly improve community detection performance and potentially be a useful approach to identifying communities |
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