Network representation learning via improved random walk with restart

Zhang, Yanan; Shen, Jian; Zhang, Ruisheng; Zhao, Zhijun

doi:10.1016/j.knosys.2023.110255

Cited by 12 publications

(2 citation statements)

References 34 publications

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“…Xue et al ( 2022 ) proposed the BiasedWalk algorithm with a preference for random walk, which can make nodes with the same semantics have closer distances in vector space. Zhang et al ( 2023 ) proposed a restartable random walk strategy to enhance the capture of both global and local structural features in networks. Khan et al ( 2021 ) proposed VECODER, a joint learning model based on variational embedding of community discovery and node representation, which utilized community aware node embedding to better detect node communities.…”

Section: Related Workmentioning

confidence: 99%

Multi-channel high-order network representation learning research

Ye,

Tang,

Zhao

et al. 2024

Front. Neurorobot.

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The existing network representation learning algorithms mainly model the relationship between network nodes based on the structural features of the network, or use text features, hierarchical features and other external attributes to realize the network joint representation learning. Capturing global features of the network allows the obtained node vectors to retain more comprehensive feature information during training, thereby enhancing the quality of embeddings. In order to preserve the global structural features of the network in the training results, we employed a multi-channel learning approach to perform high-order feature modeling on the network. We proposed a novel algorithm for multi-channel high-order network representation learning, referred to as the Multi-Channel High-Order Network Representation (MHNR) algorithm. This algorithm initially constructs high-order network features from the original network structure, thereby transforming the single-channel network representation learning process into a multi-channel high-order network representation learning process. Then, for each single-channel network representation learning process, the novel graph assimilation mechanism is introduced in the algorithm, so as to realize the high-order network structure modeling mechanism in the single-channel network representation learning. Finally, the algorithm integrates the multi-channel and single-channel mechanism of high-order network structure joint modeling, realizing the efficient use of network structure features and sufficient modeling. Experimental results show that the node classification performance of the proposed MHNR algorithm reaches a good order on Citeseer, Cora, and DBLP data, and its node classification performance is better than that of the comparison algorithm used in this paper. In addition, when the vector length is optimized, the average classification accuracy of nodes of the proposed algorithm is up to 12.24% higher than that of the DeepWalk algorithm. Therefore, the node classification performance of the proposed algorithm can reach the current optimal order only based on the structural features of the network under the condition of no external feature supplementary modeling.

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Section: Related Workmentioning

confidence: 99%

Multi-channel high-order network representation learning research

Ye,

Tang,

Zhao

et al. 2024

Front. Neurorobot.

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“…Canturk et al [1] used random walk for location recommendation on model subgraphs. Zhang et al [28] effectively captured the global and local structure of the network by an improved random walk strategy.…”

Section: Introductionmentioning

confidence: 99%

RWESA-GNNR: Fusing Random Walk Embedding and Sentiment Analysis for Graph Neural Network Recommendation

Gu,

Xu,

Liu

2024

ITC

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A graph neural network-based recommendation system treats the relationship between user items as a graph, and achieves deep feature mining by modelling the graph nodes. However, the complexity of the features of graph neural network-based recommendation systems brings poor interpretability and suffers from data sparsity problems. To address the above problems, a graph convolutional neural network recommendation model (RWESA-GNNR) based on random walk embedding combined with sentiment analysis is proposed. Firstly, a random walk-based matrix factorization is designed as the initial embedding. Secondly, the user and item nodes are modelled using a convolutional neural network with an injected attention mechanism. Then, sentiment analysis is performed on the review text, and attention mechanism is introduced to fuse text sentiment features and semantic features. Finally, node features and text features are aggregated to generate recommendation results. The experimental results show that our proposed algorithm outperforms traditional recommendation algorithms and other graph neural network-based recommendation algorithms in terms of recommendation results, with an improvement of about 2.43%-5.75%.

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