Community Detection Based on Graph Representation Learning in Evolutionary Networks

Chen, Dongming; Nie, Mingshuo; Wang, Jie; Kong, Yun; Wang, Dongqi; Huang, X.

doi:10.3390/app11104497

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…This automatic setting of control parameters allows for more efficient exploration of the network structure and the obtaining of node vectors for application to link prediction tasks. The walk control parameters, the transfer probability of the nodes and the nonnormalized transfer probability of the algorithms in this paper are defined in Equations ( 4)- (7). Definition 4.…”

Section: Graph Embedding Methods Based On Biased Walking For Link Pre...mentioning

confidence: 99%

“…Link prediction is an important part of complex network analysis [1], which aims at predicting missing, spurious, or new links in the current structure of the network by using the structure information and attribute information of a given network [2]. Link prediction plays an important role in social network analysis [3,4], network reconstruction [5], and network evolution mechanisms [6][7][8]. In addition, link prediction in the theoretical analysis assists in comprehending the mechanism of propagation and diffusion of information [9].…”

Section: Introductionmentioning

confidence: 99%

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Graph Embedding Method Based on Biased Walking for Link Prediction

2022

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Link prediction is an essential and challenging problem in research on complex networks, which can provide research tools and theoretical supports for the formation and evolutionary mechanisms of networks. Existing graph representation learning methods based on random walks usually ignore the influence of local network topology on the transition probability of walking nodes when predicting the existence of links, and the sampling strategy of walking nodes during random walks is uncontrolled, which leads to the inability of these methods to effectively learn high-quality node vectors to solve the link prediction problem. To address the above challenges, we propose a novel graph embedding method for link prediction. Specifically, we analyze the evolution mechanism of links based on triadic closure theory and use the network clustering coefficient to represent the aggregation ability of the network’s local structure, and this adaptive definition of the aggregation ability of the local structure enables control of the walking strategy of nodes in the random walking process. Finally, node embedding generated based on biased walking paths is employed to solve the link prediction problem. Extensive experiments and analyses show that the TCW algorithm provides high accuracy across a diverse set of datasets.

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Section: Graph Embedding Methods Based On Biased Walking For Link Pre...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Embedding Method Based on Biased Walking for Link Prediction

2022

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“…Community detection is one of the most active topics in the field of graph mining and network science [8], where the community structure can represent the implicit structure in the network [9]. Community discovery algorithms can find the most reasonable division of communities in a network by the observed topology, thus providing support for researchers to analyze the network topology.…”

Section: Related Workmentioning

confidence: 99%

Community Discovery Algorithm Based on Multi-Relationship Embedding

Chen¹,

Nie²,

Wang³

et al. 2023

Computer Systems Science and Engineering

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Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm.

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“…Finally, more conceptual studies are also available. In particular, Turner et al [13] present a modular dynamic neural network architecture for continual learning that can deal with the phenomenon of catastrophic forgetting, and Chen et al [14] analyze the temporal structures in evolutionary networks. In the latter case, the authors propose a community detection algorithm based on graph representation learning that uses a Laplacian matrix to extract the node relationship data of the edges of the network structure that are directly connected at the preceding time slice.…”

Section: Artificial Intelligence Applications and Innovationmentioning

confidence: 99%