Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifold

• URL: http://arxiv.org/abs/2307.04514v1
• Date: Mon, 10 Jul 2023 12:20:50 GMT
• Title: Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifold
• Authors: Tuc Nguyen-Van, Dung D. Le, The-Anh Ta
• Abstract summary: In graph representation learning, the complex geometric structure of the input graph, e.g. hidden relations among nodes, is well captured in embedding space. Standard Euclidean embedding spaces have a limited capacity in representing graphs of varying structures. A promising candidate for the faithful embedding of data with varying structure is product manifold embedding spaces.
• Score: 4.640835690336652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In graph representation learning, it is important that the complex geometric structure of the input graph, e.g. hidden relations among nodes, is well captured in embedding space. However, standard Euclidean embedding spaces have a limited capacity in representing graphs of varying structures. A promising candidate for the faithful embedding of data with varying structure is product manifolds of component spaces of different geometries (spherical, hyperbolic, or euclidean). In this paper, we take a closer look at the structure of product manifold embedding spaces and argue that each component space in a product contributes differently to expressing structures in the input graph, hence should be weighted accordingly. This is different from previous works which consider the roles of different components equally. We then propose WEIGHTED-PM, a data-driven method for learning embedding of heterogeneous graphs in weighted product manifolds. Our method utilizes the topological information of the input graph to automatically determine the weight of each component in product spaces. Extensive experiments on synthetic and real-world graph datasets demonstrate that WEIGHTED-PM is capable of learning better graph representations with lower geometric distortion from input data, and performs better on multiple downstream tasks, such as word similarity learning, top-$k$ recommendation, and knowledge graph embedding.

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