Towards Multi-view Consistent Graph Diffusion

Jielong Lu, Zhihao Wu, Zhaoliang Chen, Zhiling Cai, Shiping Wang

Abstract:

Facing the increasing heterogeneity of data in the real world, multi-view learning has become a crucial area of research. Many researchers favor using graph convolutional networks for their adeptness at modeling both the topology and the attributes.
However, these approaches typically only consider the construction of static topologies within individual views, overlooking the potential relationships between views in multi-view data. Furthermore, there is a glaring absence of theoretical guidance for constructing topologies of multi-view data, leaving uncertainties about whether graph embeddings are progressing toward the desired state.
To tackle these challenges, we introduce a framework named energy-constrained multi-view graph diffusion.
This approach establishes a mathematical correspondence between multi-view data and graph convolution via graph diffusion. It derives a feature propagation process with inter-view perception by considering both inter- and intra-view feature flows across the entire system, treating multi-view data as a holistic entity. Additionally, an energy function is introduced to guide the inter- and intra-view diffusion functions, ensuring that the representations converge towards global consistency.
The empirical research on several benchmark datasets substantiates the benefits of the proposed method and demonstrates its significant performance improvement.

Primary Subject Area: [Experience] Multimedia Applications

Secondary Subject Area: [Content] Multimodal Fusion

Relevance To Conference: This paper is designed to uncover and articulate the latent dependencies that exist both within and across the multiple views of datasets, as substantiated through empirical analysis in various multi-view scenarios.

Submission Number: 3125