.png){kind=icon}
Return
Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs
.png){kind=icon}
OA
AI
DOI：10.1016/j.neunet.2024.106225.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi -relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta -paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi -Order Graph Convolutional Network (AMOGCN), which automatically explores meta -paths that involve multi -hop neighbors by aggregating multi -order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi -order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one -layer simplifying graph convolutional network with the learned multi -order adjacency matrix, which is equivalent to the cross -hop node information propagation with multilayer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi -supervised classification performance compared with state-of-the-art competitors.
Keywords:
Heterogeneous graphs
Graph convolutional networks
Multi-order adjacency matrix
Semi-supervised classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.3
Papers:
8.2K
Citations:
3.0W
Organization
Cited Papers
Graph embedding clustering: Graph attention auto-encoder with cluster-specificity distribution.png)
NEURAL NETWORKSOAAI
NEURAL NETWORKSIF6.3
OAAIShareSaveMulti-granularity heterogeneous graph attention networks for extractive document summarizationNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
ShareSave
ShareSaveShareSaveShareSave
View more