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MentorGNN: Deriving Curriculum for Pre-Training GNNs

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Jingrui HeAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 2721 - 2731

https://doi.org/10.1145/3511808.3557393

Published: 17 October 2022 Publication History

Abstract

Graph pre-training strategies have been attracting a surge of attention in the graph mining community, due to their flexibility in parameterizing graph neural networks (GNNs) without any label information. The key idea lies in encoding valuable information into the backbone GNNs, by predicting the masked graph signals extracted from the input graphs. In order to balance the importance of diverse graph signals (e.g., nodes, edges, subgraphs), the existing approaches are mostly hand-engineered by introducing hyperparameters to re-weight the importance of graph signals. However, human interventions with sub-optimal hyperparameters often inject additional bias and deteriorate the generalization performance in the downstream applications. This paper addresses these limitations from a new perspective, i.e., deriving curriculum for pre-training GNNs. We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs with diverse structures and disparate feature spaces. To comprehend heterogeneous graph signals at different granularities, we propose a curriculum learning paradigm that automatically re-weighs graph signals in order to ensure a good generalization in the target domain. Moreover, we shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs. Extensive experiments on a wealth of real graphs validate and verify the performance of MentorGNN.

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Index Terms

MentorGNN: Deriving Curriculum for Pre-Training GNNs
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
      2. Neural networks

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CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

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ISBN:9781450392365

DOI:10.1145/3511808

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Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
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Emory University, Atlanta, USA

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Cited By

Mo ZLuo TPan SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Graph Principal Flow Network for Conditional Graph GenerationProceedings of the ACM Web Conference 202410.1145/3589334.3645570(768-779)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645570
Zheng LZhou DTong HXu JZhu YHe J(2024)Fairgen: Towards Fair Graph Generation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00181(2285-2297)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00181
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