Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion

Abstract

The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across the graph, facilitating the exploitation of consistency in multi-view data. However, most existing GCN-based multi-view methods suffer from several limitations. First, current approaches predominantly rely on KNN for topology construction, where the artificial selection of the k value significantly constrains the effective exploitation of inter-node consistency. Second, the inter-feature consistency within individual views is often overlooked, which adversely affects the quality of the final embedding representations. Moreover, these methods fail to fully utilize inter-view consistency as the fusion of embedded representations from multiple views is often implemented after the intra-view graph convolutional operation. Collectively, these issues limit the model's capacity to fully capture inter-node, inter-feature and inter-view consistency. To address these issues, this paper proposes the multi-view graph convolutional network with fully leveraging consistency via GB-based topology construction, feature enhancement and interactive fusion (MGCN-FLC). MGCN-FLC can fully utilize three types of consistency via the following three modules to enhance learning ability:The topology construction module based on the granular ball algorithm, which clusters nodes into granular balls with high internal similarity to capture inter-node consistency;The feature enhancement module that improves feature representations by capturing inter-feature consistency;The interactive fusion module that enables each view to deeply interact with all other views, thereby obtaining more comprehensive inter-view consistency. Experimental results on nine datasets show that the proposed MGCN-FLC outperforms state-of-the-art semi-supervised node classification methods.

Figures (9)

• Figure 1: MGCN-FLC consists of three modules: the topology construction module (TC), the feature enhancement module (FE) and the interactive fusion module (IF). Specifically, the TC module constructs the topology by establishing the inter-GB and intra-GB full connections between nodes, providing an effective pathway for information propagation in the GCN. The FE module applies mixed pooling to the stacking result of the similarity feature matrix and the original feature matrix, generating the enhanced feature representations. Subsequently, the enhanced feature representations generated by FE module from multiple views are encoding into the same dimension. Based on the encoder's output, the IF module computes and aggregates interactive features between two distinct views to ultimately generate node embeddings. Finally, the topology constructed by the TC module and the node embeddings generated by the IF module are jointly fed as input into a standard GCN for node prediction.
• Figure 2: ACC of all methods as the ratio of labeled data ranges in {0.05, 0.10, … , 0.5} on the nine datasets.
• Figure 3: F1-score of all methods as the ratio of labeled data ranges in {0.05, 0.10, … , 0.5} on the nine datasets.
• Figure 4: The ACC and F1-score of MGCN-FLC w.r.t. hyperparameter $\alpha$ on datasets.
• Figure 5: The ACC and F1-score of MGCN-FLC w.r.t. hyperparameter $\beta$ on datasets.