TY - GEN
T1 - Heterogeneous Graph Attribute Completion via Efficient Meta-path Context-Aware Learning
AU - Zhang, Lijun
AU - Chen, Geng
AU - Wang, Qingyue
AU - Wang, Peng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
PY - 2024
Y1 - 2024
N2 - Heterogeneous graph attribute completion (HGAC) is an emerging research direction and has drawn increasing research attention in recent years. Although making significant progress, existing HGAC methods suffer from three limitations, including (i) the ignorance of valuable meta-path information during the graph node encoding, (ii) insufficient graph context learning as only neighboring nodes are considered during the completion process, and (iii) low efficiency due to the use of heterogeneous graph neural network for downstream tasks. To address these limitations, we propose an efficient meta-path context-aware learning framework to improve the completion of missing attributes of heterogeneous graphs. In our framework, we introduce a collaborative meta-path-driven embedding scheme, which incorporates valuable meta-path prior knowledge during node sampling. Furthermore, we devise a context-aware attention mechanism to complete the missing node attributes, which captures the information of non-neighbor nodes. Finally, we adopt graph attention networks for downstream tasks, effectively improving computational efficiency. Extensive experiments on three real-world heterogeneous graph datasets demonstrate that our framework outperforms state-of-the-art HGAC methods remarkably.
AB - Heterogeneous graph attribute completion (HGAC) is an emerging research direction and has drawn increasing research attention in recent years. Although making significant progress, existing HGAC methods suffer from three limitations, including (i) the ignorance of valuable meta-path information during the graph node encoding, (ii) insufficient graph context learning as only neighboring nodes are considered during the completion process, and (iii) low efficiency due to the use of heterogeneous graph neural network for downstream tasks. To address these limitations, we propose an efficient meta-path context-aware learning framework to improve the completion of missing attributes of heterogeneous graphs. In our framework, we introduce a collaborative meta-path-driven embedding scheme, which incorporates valuable meta-path prior knowledge during node sampling. Furthermore, we devise a context-aware attention mechanism to complete the missing node attributes, which captures the information of non-neighbor nodes. Finally, we adopt graph attention networks for downstream tasks, effectively improving computational efficiency. Extensive experiments on three real-world heterogeneous graph datasets demonstrate that our framework outperforms state-of-the-art HGAC methods remarkably.
KW - Attribute completion
KW - Graph neural networks
KW - Graph node classification
KW - Heterogeneous graph learning
UR - https://www.scopus.com/pages/publications/85181769083
U2 - 10.1007/978-981-99-8546-3_32
DO - 10.1007/978-981-99-8546-3_32
M3 - 会议稿件
AN - SCOPUS:85181769083
SN - 9789819985456
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 396
EP - 408
BT - Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings
A2 - Liu, Qingshan
A2 - Wang, Hanzi
A2 - Ji, Rongrong
A2 - Ma, Zhanyu
A2 - Zheng, Weishi
A2 - Zha, Hongbin
A2 - Chen, Xilin
A2 - Wang, Liang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023
Y2 - 13 October 2023 through 15 October 2023
ER -