Community detection in error-prone environments based on particle cooperation and competition with distance dynamics

Wang, Benyu; Gu, Yanfeng; Zheng, Di-Wen

doi:10.1016/j.physa.2022.128178

Cited by 7 publications

(3 citation statements)

References 60 publications

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“…We cannot change the qualitative topological structure of scaleless networks by accidentally removing even a large part of the nodes. Wang et al [31] present a community detection method based on particle cooperation and competition.…”

Section: Related Workmentioning

confidence: 99%

A novel robust memetic algorithm for dynamic community structures detection in complex networks

Ranjkesh,

Masoumi,

Hashemi

2024

World Wide Web

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Networks in the real world are dynamic and evolving. The most critical process in networks is to determine the structure of the community, based on which we can detect hidden communities in a complex network. The design of strong network structures is of great importance, meaning that a system must maintain its function in the face of attacks and failures and have a strong community structure. In this paper, we proposed the robust memetic algorithm and used the idea to optimize the detection of dynamic communities in complex networks called RDMA_NET (Robust Dynamic Memetic Algorithm). In this method, we work on dynamic data that affects the two main parts of the initial population value and the calculation of the evaluation function of each population, and there is no need to determine the number of communities in advance. We used two sets of real-world networks and the LFR dataset. The results show that our proposed method, RDMA_Net, can nd a better solution than modern approaches and provide near-optimal performance in search of network topologies with a strong community structure.

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Section: Related Workmentioning

confidence: 99%

A novel robust memetic algorithm for dynamic community structures detection in complex networks

Ranjkesh,

Masoumi,

Hashemi

2024

World Wide Web

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“…Among many methods, dynamics-based methods constitute a significant branch of community detection algorithms, which reveal the community structure by modeling the interactions between nodes in a network. Currently, some of the mainstream dynamic-based community detection algorithms include label propagation, random walk, Markov clustering, dynamic distance, and particle competition [7].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity

Huang,

2023

Applied Sciences

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Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks.

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“…The process of finding corresponding anchor nodes is also called network alignment (NA). Comparative studies of specific tasks, such as cross-network recommendation [5], mutual community detection [6], and genetic disease classification [7], can be conducted at the systems level with help of NA.…”

Section: Introductionmentioning

confidence: 99%

Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure

Wang

Shao

et al. 2023

Algorithms

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Network alignment (NA) offers a comprehensive way to build associations between different networks by identifying shared nodes. While the majority of current NA methods rely on the topological consistency assumption, which posits that shared nodes across different networks typically have similar local structures or neighbors, we argue that anchor nodes, which play a pivotal role in NA, face a more challenging scenario that is often overlooked. In this paper, we conduct extensive statistical analysis across networks to investigate the connection status of labeled anchor node pairs and categorize them into four situations. Based on our analysis, we propose an end-to-end network alignment framework that uses node representations as a distribution rather than a point vector to better handle the structural diversity of networks. To mitigate the influence of specific nodes, we introduce a mask mechanism during the representation learning process. In addition, we utilize meta-learning to generalize the learned information on labeled anchor node pairs to other node pairs. Finally, we perform comprehensive experiments on both real-world and synthetic datasets to confirm the efficacy of our proposed method. The experimental results demonstrate that the proposed model outperforms the state-of-the-art methods significantly.

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Community detection in error-prone environments based on particle cooperation and competition with distance dynamics

Cited by 7 publications

References 60 publications

A novel robust memetic algorithm for dynamic community structures detection in complex networks

A novel robust memetic algorithm for dynamic community structures detection in complex networks

Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity

Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure

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