Graph as Point Set

• URL: http://arxiv.org/abs/2405.02795v2
• Date: Thu, 30 May 2024 18:44:49 GMT
• Title: Graph as Point Set
• Authors: Xiyuan Wang, Pan Li, Muhan Zhang,
• Abstract summary: This paper introduces a novel graph-to-set conversion method that transforms interconnected nodes into a set of independent points. It enables using set encoders to learn from graphs, thereby significantly expanding the design space of Graph Neural Networks. To demonstrate the effectiveness of our approach, we introduce Point Set Transformer (PST), a transformer architecture that accepts a point set converted from a graph as input.
• Score: 31.448841287258116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph is a fundamental data structure to model interconnections between entities. Set, on the contrary, stores independent elements. To learn graph representations, current Graph Neural Networks (GNNs) primarily use message passing to encode the interconnections. In contrast, this paper introduces a novel graph-to-set conversion method that bijectively transforms interconnected nodes into a set of independent points and then uses a set encoder to learn the graph representation. This conversion method holds dual significance. Firstly, it enables using set encoders to learn from graphs, thereby significantly expanding the design space of GNNs. Secondly, for Transformer, a specific set encoder, we provide a novel and principled approach to inject graph information losslessly, different from all the heuristic structural/positional encoding methods adopted in previous graph transformers. To demonstrate the effectiveness of our approach, we introduce Point Set Transformer (PST), a transformer architecture that accepts a point set converted from a graph as input. Theoretically, PST exhibits superior expressivity for both short-range substructure counting and long-range shortest path distance tasks compared to existing GNNs. Extensive experiments further validate PST's outstanding real-world performance. Besides Transformer, we also devise a Deepset-based set encoder, which achieves performance comparable to representative GNNs, affirming the versatility of our graph-to-set method.

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