Network Representation Learning Based on Community and Text Features

Zhu, Yu; Ye, Zhonglin; Zhao, Haixing; Zhang, Ke

doi:10.1007/978-3-030-01716-3_29

Cited by 1 publication

(2 citation statements)

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“…Wang et al Wang et al (2017) point out that for two nodes within a community, even if they only have a weak relationship in the microscopic structure due to the data sparsity issue, their similarity will also be strengthened by the community structure constraint. Hence, Zhu et al Zhu et al (2018) propose CTDW, which incorporates the community features and text features of nodes into NRL under the framework of matrix factorization. Nevertheless, the design of matrix factorization requires a high computational cost.…”

Section: Related Workmentioning

confidence: 99%

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Network Representation Learning With Community Awareness and Its Applications in Brain Networks

Shi

Liu

2022

Front. Physiol.

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Previously network representation learning methods mainly focus on exploring the microscopic structure, i.e., the pairwise relationship or similarity between nodes. However, the mesoscopic structure, i.e., community structure, an essential property in real networks, has not been thoroughly studied in the network representation learning. We here propose a deep attributed network representation learning with community awareness (DANRL-CA) framework. Specifically, we design a neighborhood enhancement autoencoder module to capture the 2-step relations between node pairs. To explore the multi-step relations, we construct a community-aware skip-gram module based on the encoder. We introduce two variants of DANRL-CA, namely, DANRL-CA-AM and DANRL-CA-CSM, which incorporate the community information and attribute semantics into node neighbors with different methods. We compare two variant models with the state-of-the-art methods on four datasets for node classification and link prediction. Especially, we apply our models on a brain network. The superiority indicates the scalability and effectiveness of our method on various networks. Compared with DANRL-CA-AM, DANRL-CA-CSM can more flexibly coordinate the role of node attributes and community information in the process of network representation learning, and shows superiority in the networks with sparse topological structure and node attributes.

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Section: Related Workmentioning

confidence: 99%

“…Evidently, mining the community information and preserving the attribute semantics are both advantageous to enhance the quality of node embeddings based on the microscopic structure. Therefore, Zhu et al Zhu et al (2018) propose the CTDW algorithm.…”

Section: Introductionmentioning

confidence: 99%

Network Representation Learning With Community Awareness and Its Applications in Brain Networks

Shi

Liu

2022

Front. Physiol.

View full text Add to dashboard Cite

show abstract

Network Representation Learning Based on Community and Text Features

Cited by 1 publication

References 14 publications

Network Representation Learning With Community Awareness and Its Applications in Brain Networks

Network Representation Learning With Community Awareness and Its Applications in Brain Networks

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