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Node classification based on Attribute Fuse Edge Features and Label Adaptive Adjustment

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  • Shang, Ronghua
  • Li, Ruolin
  • Wang, Chi
  • Zhang, Weitong
  • Xu, Songhua
  • Feng, Dongzhu

Abstract

Most of existing graph representation learning methods only extract information from nodes and ignore the hidden information of edges. Nodes carry weak structural information thus affecting the specificity of node embeddings.To solve these problems, this paper proposes a node classification algorithm based on Attribute Fuse Edge Features and Label Adaptive Adjustment (AFEF_LAA).Firstly, Intimate-Relationship-Attribute of node is designed based on edge embeddings.Rz-Cos rule is constructed to perform the similarity metric between nodes and their neighbors to select intimate neighbor nodes.After that Reverse-TransE is constructed to encode embedding vectors of the edges connected to intimate neighborhood nodes.Secondly, a multi-fusion method based on smoothed neighborhood information is constructed.Each node in the original graph is smoothed together with its neighbor nodes.The smoothed original graph is multi-fused with multiple twin graphs. Finally, a strategy of label adaptive adjustment is proposed to select the label embedding vectors for input to the next-generation trainer by comparing accuracy.This strategy can improve the quality of graph embeddings while effectively avoiding the overfitting problem when processing high-dimensional data.AFEF_LAA is compared with the state-of-the-art algorithms on six graph datasets.Experimental results show that AFEF_LAA can achieve higher node classification accuracy.

Suggested Citation

  • Shang, Ronghua & Li, Ruolin & Wang, Chi & Zhang, Weitong & Xu, Songhua & Feng, Dongzhu, 2024. "Node classification based on Attribute Fuse Edge Features and Label Adaptive Adjustment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
  • Handle: RePEc:eee:phsmap:v:654:y:2024:i:c:s037843712400640x
    DOI: 10.1016/j.physa.2024.130131
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    References listed on IDEAS

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    1. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    2. Zhang, Jie & Song, Chunyue & Cao, Shan & Zhang, Chun, 2023. "FDST-GCN: A Fundamental Diagram based Spatiotemporal Graph Convolutional Network for expressway traffic forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    3. Shang, Ronghua & Zhang, Weitong & Zhang, Jingwen & Feng, Jie & Jiao, Licheng, 2022. "Local community detection based on higher-order structure and edge information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    4. Xu, Xinpeng & Yang, Chen & Wu, Weiguo, 2024. "Representation learning and Graph Convolutional Networks for short-term vehicle trajectory prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    5. Shang, Ronghua & Luo, Shuang & Zhang, Weitong & Stolkin, Rustam & Jiao, Licheng, 2016. "A multiobjective evolutionary algorithm to find community structures based on affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 203-227.
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