Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one. Although a few efforts have been made by utilizing Graph Convolution Networks (GCNs) to learn node representations and model correlations between multiple labels in the embedding space, they still suffer from the ambiguous feature and ambiguous topology induced by multiple labels, which reduces the credibility of the messages delivered in graphs and overlooks the label correlations on graph data. Therefore, it is crucial to reduce the ambiguity and empower the GCNs for accurate classification. However, this is quite challenging due to the requirement of retaining the distinctiveness of each label while fully harnessing the correlation between labels simultaneously. To address these issues, in this paper, we propose a Correlation-aware Graph Convolutional Network (CorGCN) for multi-label node classification. By introducing a novel Correlation-Aware Graph Decomposition module, CorGCN can learn a graph that contains rich label-correlated information for each label. It then employs a Correlation-Enhanced Graph Convolution to model the relationships between labels during message passing to further bolster the classification process. Extensive experiments on five datasets demonstrate the effectiveness of our proposed CorGCN.

翻译：多标签节点分类是图挖掘领域中一个重要但尚未充分探索的方向，因为现实世界中的许多节点属于多个类别而非单一类别。尽管已有研究尝试利用图卷积网络（GCNs）学习节点表示并在嵌入空间中建模多个标签之间的相关性，但这些方法仍受多标签引起的特征模糊性和拓扑模糊性困扰，这降低了图中传递信息的可信度，并忽视了图数据上的标签相关性。因此，减少模糊性并增强GCNs以实现精确分类至关重要。然而，这极具挑战性，因为需要在充分挖掘标签间相关性的同时保持每个标签的独特性。为解决这些问题，本文提出一种用于多标签节点分类的相关感知图卷积网络（CorGCN）。通过引入新颖的相关感知图分解模块，CorGCN能够为每个标签学习包含丰富标签相关信息的图结构。随后，该网络采用相关增强图卷积在消息传递过程中建模标签间关系，以进一步强化分类过程。在五个数据集上的大量实验验证了我们提出的CorGCN的有效性。