Zero-shot Node Classification with Decomposed Graph Prototype Network

• URL: http://arxiv.org/abs/2106.08022v1
• Date: Tue, 15 Jun 2021 10:13:20 GMT
• Title: Zero-shot Node Classification with Decomposed Graph Prototype Network
• Authors: Zheng Wang, Jialong Wang, Yuchen Guo, Zhiguo Gong
• Abstract summary: We study a zero-shot node classification (ZNC) problem which has a two-stage nature. For the first stage, we give a novel quantitative CSDs evaluation strategy based on estimating the real class relationships. For the second stage, we propose a novel Decomposed Graph Prototype Network (DGPN) method.
• Score: 33.24920910739568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Node classification is a central task in graph data analysis. Scarce or even no labeled data of emerging classes is a big challenge for existing methods. A natural question arises: can we classify the nodes from those classes that have never been seen? In this paper, we study this zero-shot node classification (ZNC) problem which has a two-stage nature: (1) acquiring high-quality class semantic descriptions (CSDs) for knowledge transfer, and (2) designing a well generalized graph-based learning model. For the first stage, we give a novel quantitative CSDs evaluation strategy based on estimating the real class relationships, so as to get the "best" CSDs in a completely automatic way. For the second stage, we propose a novel Decomposed Graph Prototype Network (DGPN) method, following the principles of locality and compositionality for zero-shot model generalization. Finally, we conduct extensive experiments to demonstrate the effectiveness of our solutions.

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