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Unified structure-aware feature learning for Graph Convolutional Network
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DOI：10.1016/j.eswa.2024.124397.png)
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Abstract
En 中文
Graph Convolutional Network (GCN), as an effective technique for processing graph -structural data, has been widely used in various fields. Label Propagation Algorithm (LPA), also used as a way for message passing on a graph, broadcasts and integrates node labels over the edges. There have been several efforts to explore the joint learning of GCN and LPA for semi -supervised classification tasks. However, they fail to consider the following two points: (1) Capture precise connections and dispel heterophilous relationships between nodes under the mutual supervision of feature and label spaces; (2) Exploit high -order information so that it has varied weight values in affecting distinct nodes. In light of this, we propose a joint framework called structure -aware feature learning for graph convolutional network, which simultaneously takes into account the above two aspects. In specific, the proposed model consists of two modules: feature aggregation and label propagation, aiming to better utilize the information from both label and feature spaces to learn more accurate node representations. As a result, two modules interact with each other to flexibly assign edge weights. Meanwhile, we adopt a structure -aware manner to provide various weights for neighbors of each order of the target node, contributing to optionally capturing high -order information. Experiments indicate that the proposed method outperforms other compared semi -supervised classification models.
Keywords:
Graph convolutional network
Semi-supervised classification
Deep learning
Structure learning
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