Enhancing Multi-view Graph Neural Network with Cross-view Confluent Message Passing

Shuman Zhuang, Sujia Huang, Wei Huang, Yuhong Chen, Zhihao Wu, Ximeng Liu

Abstract:

With the growing diversity of data sources, multi-view learning methods have attracted considerable attention.
Among these, by modeling the multi-view data as multi-view graphs, multi-view Graph Neural Networks (GNNs) have shown encouraging performance on various multi-view learning tasks.
The message passing is the critical mechanism empowering GNNs with superior capacity to process complex graph data.
However, most multi-view GNNs are designed on the well-established overall framework, overlooking the intrinsic challenges of the message passing on multi-view scenarios.
To clarify this, we first revisit the message passing mechanism from a graph smoothing perspective, revealing the key to designing a multi-view message passing.
Following the analysis, in this paper, we propose an enhanced GNN framework termed Confluent Graph Neural Networks (CGNN), with Cross-view Confulent Message Passing (CCMP) tailored for multi-view learning.
Inspired by the optimization of an improved multi-view graph smoothing problem, CCMP contains three sub-modules that enable the interaction between graph structures and consistent representations, which makes it aware of consistency and complementarity information across views.
Extensive experiments on four types of data including multi-modality data demonstrate that our proposed model exhibits superior effectiveness and robustness.

Primary Subject Area: [Experience] Multimedia Applications

Secondary Subject Area: [Content] Multimodal Fusion

Relevance To Conference: Our work proposes an enhanced GNN framework termed Confluent Graph Neural Networks (CGNN), with Cross-view Confulent Message Passing (CCMP) tailored for multi-view learning. Extensive experiments on four types of data including multi-modality data demonstrate that our proposed model exhibits superior effectiveness and robustness. Our proposed model facilitates multimedia/multimodal technology development by leveraging enhanced capabilities of CGNN with CCMP designed for multi-view learning scenarios.

Submission Number: 4881