Task-oriented attributed network embedding by multi-view features

Lai, Darong; Wang, Sheng; Chong, Zhihong; Wu, Weiwei; Nardini, Christine

doi:10.1016/j.knosys.2021.107448

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…Researchers have gradually focused on expressing network nodes with low-latitude, high-density spatial vectors, thereby maintaining the structure and feature information of the original network. Learned feature vectors are represented such as by graph-based classification, clustering, and link prediction [9][10][11]. As a research direction of graph neural networks, graph embedding has also attracted attention [12].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation

Zhang

et al. 2022

Mathematics

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The node embedding method enables network structure feature learning and representation for social network community detection. However, the traditional node embedding method only focuses on a node’s individual feature representation and ignores the global topological feature representation of the network. Traditional community detection methods cannot use the static node vector from the traditional node embedding method to calculate the dynamic features of the topological structure. In this study, an incremental dynamic community detection model based on a graph neural network node embedding representation is proposed, comprising the following aspects. A node embedding model based on influence random walk improves the information enrichment of the node feature vector representation, which improves the performance of the initial static community detection, whose results are used as the original structure of dynamic community detection. By combining a cohesion coefficient and ordinary modularity, a new modularity calculation method is proposed that uses an incremental training method to obtain node vector representation to detect a dynamic community from the perspectives of coarse- and fine-grained adjustments. A performance analysis based on two dynamic network datasets shows that the proposed method performs better than benchmark algorithms based on time complexity, community detection accuracy, and other indicators.

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Section: Introductionmentioning

confidence: 99%

Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation

Zhang

et al. 2022

Mathematics

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Multi-view dual-channel graph convolutional networks with multi-task learning

Ling,

Li,

Liu

et al. 2023

Complex Intell. Syst.

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Network embedding has been extensively used in several practical applications and achieved great success. However, existing studies mainly focus on single task or single view and cannot obtain deeper relevant information for accomplishing tasks. In this paper, a novel approach is proposed to address the problem of insufficient information consideration in network embedding, which is termed multi-task-oriented adaptive dual-channel graph convolutional network (TAD-GCN). We firstly use kNN graph construction method to generate three views for each network dataset. Then, the proposed TAD-GCN contains dual-channel GCN which can extract the specific and shared embeddings from multiple views simultaneously, and attention mechanism is adopted to fuse them adaptively. In addition, we design similarity constraint and difference constraint to further enhance their semantic similarity and ensure that they capture the different information. Lastly, a multi-task learning module is introduced to solve multiple tasks simultaneously and optimize the model with its losses. The experimental results demonstrate that our model TAD-GCN not only completes multiple downstream tasks at the same time, but also achieves excellent performance compared with eight state-of-the-art methods.

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