Modularized tri-factor nonnegative matrix factorization for community detection enhancement

Yan, Chao; Chang, Zhenhai

doi:10.1016/j.physa.2019.122050

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…The cooperative speed of each vehicle is calculated in the metric RCL as shown in Reference 25. The metric is rewarded or penalized if its speed is less than or greater than the difference between the speed of the neighbor vehicle and the group's average speed, as shown in Equation (18). is the change in the RCL value at any given time.…”

Section: Modularized Relative Cooperative Speedmentioning

confidence: 99%

“…The graph representation of the VANET is not the true network representation. Spectral clustering employs the distance adjacency matrix, which does not capture all of the network's information 18 . These two factors in the cluster generation problem motivated us to look for a more realistic network representation alternative.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional network mapping as a two‐node graph fails to account for a vehicle's dependence on all its neighbors. A vehicular network is a social network and spectral clustering is used to determine the cluster size and cluster them. The adjacency matrix having the distance value between each vehicle at the cell is used to determine the clusters; however, the community information is less mapped 18 Metrics such as speed, mobility, and neighborhood are not defined relatively.…”

Section: Introductionmentioning

confidence: 99%

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Modularized hypergraph clustering scheme model for stableVANET

Kumar

Dahiya

Singh³

2022

Concurrency and Computation

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Summary VANET transforms vehicles into a social environment, allowing for maximum security and cooperative data transmission. Each vehicle is assigned a cluster, which transfers data to the cluster head (CH). The efficiency of a VANET network is determined by two factors: efficient vehicle groupings and optimal CH selection. This article proposes a novel modularized hypergraph clustering scheme in which the VANET is represented as a hypergraph network clustered using tensor trace maximization with the modularity matrix as the feed. CH is chosen from each cluster based on four vehicle parameters: vehicle speed about neighbors, consensus trust score, link lifetime, and connectivity level. For the final CH selection, all parameters are converted to eigenvalues. The fuzzy decision‐making scheme selects the best CH for all vehicles in that cluster based on a set of four parameters. The proposed algorithm for selecting the stable CH for improved network performance has also been validated and significantly improved using state‐of‐the‐art schemes.

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Section: Modularized Relative Cooperative Speedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modularized hypergraph clustering scheme model for stableVANET

Kumar

Dahiya

Singh³

2022

Concurrency and Computation

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“…As revealed in previous work, informative node attributes can help to find meaningful groups of users with similar interests, backgrounds, or purposes, which can further effectively support applications in recommendation, sentiment analysis, and user profiling [11]. Moreover, realistic complex networks often contain multiple structures, in addition to the traditional community structure, also known as assortative mixing, i.e., defined as a structure with tight intracommunity node links and sparse inter-community links, such as the classical citation network Cora dataset; they also contain multiple complex network structures, such as the bipartite network [12] generated by the English lexical link network Adjnoun, and mixture structures containing both structures, also called disassortative mixing [13]. Mining the various underlying structures and interaction patterns between communities in a network is of great theoretical and practical significance for understanding the function of networks, discovering hidden patterns and predicting the behavior of individuals in the network.…”

Section: Introductionmentioning

confidence: 99%

General Community Detection in Attributed Networks with Consistent-Module Constrained Nonnegative Matrix Factorization

Liu

Wei

et al. 2022

Wireless Communications and Mobile Computing

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Nonnegative matrix factorization (NMF) model has been successfully applied to discover latent community structures due to its good performance and interpretability advantages in extracting hidden patterns. However, most previous studies explore only the structural information of the network while ignoring the rich attributes. Besides, they aim at detecting densely connected communities (also called community structures) and fail to identify general structures, such as bipartite structures and mixture structures. In this paper, we research on general structure discovery and propose a new method GCDNMF (General Community Detection based on Nonnegative Matrix Factorization), which integrates structural information and node attributes through consistency module constraint to capture the community interactions. It can discover the general community structures of nodes by iteratively updating the community-interaction matrix and the node-membership matrix. We also introduce matrix initialization based on centrality and dispersion of nodes for center selection to reduce the sensitivity of random initialization. Experimental results on real-world networks with a variety of characteristics validate the performance of our approach, especially on networks with general structures. In addition, the associated initialization evaluations demonstrate the effectiveness of our method in obtaining stable results.

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“…Additionally, the algorithm was based on deep learning was proposed [30]. As a valid method in unsupervised learning, Nonnegative Matrix Factorization (NMF) has also been gradually applied to analyze community structure [31,32]. Although the algorithm that is based on NMF has good interpretability, it usually needs the prior knowledge of the number of communities in the network, but the number of communities is generally unknown.…”

Section: Introductionmentioning

confidence: 99%

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Zhang

Zhao

et al. 2021

Entropy

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Community detection is of great significance in understanding the structure of the network. Label propagation algorithm (LPA) is a classical and effective method, but it has the problems of randomness and instability. An improved label propagation algorithm named LPA-MNI is proposed in this study by combining the modularity function and node importance with the original LPA. LPA-MNI first identify the initial communities according to the value of modularity. Subsequently, the label propagation is used to cluster the remaining nodes that have not been assigned to initial communities. Meanwhile, node importance is used to improve the node order of label updating and the mechanism of label selecting when multiple labels are contained by the maximum number of nodes. Extensive experiments are performed on twelve real-world networks and eight groups of synthetic networks, and the results show that LPA-MNI has better accuracy, higher modularity, and more reasonable community numbers when compared with other six algorithms. In addition, LPA-MNI is shown to be more robust than the traditional LPA algorithm.

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Modularized tri-factor nonnegative matrix factorization for community detection enhancement

Cited by 11 publications

References 31 publications

Modularized hypergraph clustering scheme model for stableVANET

Modularized hypergraph clustering scheme model for stableVANET

General Community Detection in Attributed Networks with Consistent-Module Constrained Nonnegative Matrix Factorization

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

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