Multi-layer network community detection model based on attributes and social interaction intensity

Li, Xiaoming; Xu, Guagquan; Jiao, Litao; Zhou, Yiming; Yu, Wei

doi:10.1016/j.compeleceng.2019.06.010

Cited by 15 publications

(10 citation statements)

References 18 publications

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“…During local community detection in a network layer, the attribute similarity between a node and its neighbors in the corresponding structure is used to define the criterion for selecting a seed node. The set of all seeds fulfilling this criterion is determined, which are then expanded to establish the communities (Li, X., Xu, G., Jiao, L., Zhou, Y., & Yu, W., 2019).…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…The attribute set of a neighboring subsystem can be represented in terms of the attributes of all nodes within that subsystem. For node Vi in layer X, the attribute set of its neighboring subsystem Cim can be computed as follows (Li, X., Xu, G., Jiao, L., Zhou, Y., & Yu, W., 2019) (1) Where N denotes the number of neighbors in Cim. The attribute similarity 𝑆𝑆 26: (𝑆𝑆 2 .…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…The attribute similarity 𝑆𝑆 26: (𝑆𝑆 2 . 𝑆𝑆 ;26 )between node Vi and its neighbor set 𝐶𝐶 26 in layer X can be calculated as the cosine of the included angle between the two corresponding vectors (Li, X., Xu, G., Jiao, L., Zhou, Y., & Yu, W., 2019;Interdonato, R., Tagarelli, A., Ienco, D., Sallaberry, A., & Poncelet, P., 2017):…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…The attribute of the neighboring subsystem for which 𝑆𝑆 426 (𝑆𝑆 2 . 𝑆𝑆 <26 ) is maximized and selected as the seed node's attribute, V0 (Li, X., Xu, G., Jiao, L., Zhou, Y., & Yu, W., 2019).…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…This model can effectively utilize node attribute information and the similarity strength information revealed by social exchanges to improve the accuracy of community detection in multi-layer networks. Unlike classical multi-layer and global community detection algorithms, this algorithm is robust on most datasets because of its modularity and computational efficiency (Li, X., Xu, G., Jiao, L., Zhou, Y., & Yu, W., 2019).…”

Section: Related Workmentioning

confidence: 99%

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A Model for Multi-Layer Dynamic Social Networks to Discover Influential Groups Based on a Combination of Meta-Heuristic Algorithm and C-Means Clustering

Naderlou¹,

Qasabeh²

2022

AzJHPC

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Science and technology are proliferating, and complex networks have become a necessity in our daily life, so separating people from complex networks built on the fundamental needs of human life is almost impossible. This research presented a multi-layer dynamic social networks model to discover influential groups based on a developing frog-leaping algorithm and C-means clustering. We collected the data in the first step. Then, we conducted data cleansing and normalization to identify influential individuals and groups using the optimal data by forming a decision matrix. Hence, we used the matrix to identify and cluster (based on phase clustering) and determined each group’s importance. The frog-leaping algorithm was used to improve the identification of influence parameters, which led to improvement in node’s importance, to discover influential individuals and groups in social networks, In the measurement and simulation of clustering section, the proposed method was contrasted against the K-means method, and its equilibrium value in cluster selection resulted from 5. The proposed method presented a more genuine improvement compared to the other methods. However, measuring precision indicators for the proposed method had a 3.3 improvement compared to similar methods and a 3.8 improvement compared to the M-ALCD primary method.

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Section: The Proposed Methodsmentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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