Interpreting and Unifying Graph Neural Networks with An Optimization Framework

• URL: http://arxiv.org/abs/2101.11859v1
• Date: Thu, 28 Jan 2021 08:06:02 GMT
• Title: Interpreting and Unifying Graph Neural Networks with An Optimization Framework
• Authors: Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, Peng Cui
• Abstract summary: Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning. In this paper, we establish a surprising connection between different propagation mechanisms with a unified optimization problem. Our proposed unified optimization framework, summarizing the commonalities between several of the most representative GNNs, opens up new opportunities for flexibly designing new GNNs.
• Score: 47.44773358082203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism which has been demonstrated effective is the most fundamental part of GNNs. Although most of GNNs basically follow a message passing manner, litter effort has been made to discover and analyze their essential relations. In this paper, we establish a surprising connection between different propagation mechanisms with a unified optimization problem, showing that despite the proliferation of various GNNs, in fact, their proposed propagation mechanisms are the optimal solution optimizing a feature fitting function over a wide class of graph kernels with a graph regularization term. Our proposed unified optimization framework, summarizing the commonalities between several of the most representative GNNs, not only provides a macroscopic view on surveying the relations between different GNNs, but also further opens up new opportunities for flexibly designing new GNNs. With the proposed framework, we discover that existing works usually utilize naive graph convolutional kernels for feature fitting function, and we further develop two novel objective functions considering adjustable graph kernels showing low-pass or high-pass filtering capabilities respectively. Moreover, we provide the convergence proofs and expressive power comparisons for the proposed models. Extensive experiments on benchmark datasets clearly show that the proposed GNNs not only outperform the state-of-the-art methods but also have good ability to alleviate over-smoothing, and further verify the feasibility for designing GNNs with our unified optimization framework.

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