FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

• URL: http://arxiv.org/abs/2205.02435v2
• Date: Sat, 7 May 2022 01:51:12 GMT
• Title: FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
• Authors: Song Wang, Yushun Dong, Xiao Huang, Chen Chen, Jundong Li
• Abstract summary: Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. We propose a novel few-shot learning framework FAITH that captures task correlations via constructing a hierarchical task graph. Experiments on four prevalent few-shot graph classification datasets demonstrate the superiority of FAITH over other state-of-the-art baselines.
• Score: 39.576675425158754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning frameworks for fast adaptations to graph classes with limited labeled graphs. Specifically, these works propose to accumulate meta-knowledge across diverse meta-training tasks, and then generalize such meta-knowledge to the target task with a disjoint label set. However, existing methods generally ignore task correlations among meta-training tasks while treating them independently. Nevertheless, such task correlations can advance the model generalization to the target task for better classification performance. On the other hand, it remains non-trivial to utilize task correlations due to the complex components in a large number of meta-training tasks. To deal with this, we propose a novel few-shot learning framework FAITH that captures task correlations via constructing a hierarchical task graph at different granularities. Then we further design a loss-based sampling strategy to select tasks with more correlated classes. Moreover, a task-specific classifier is proposed to utilize the learned task correlations for few-shot classification. Extensive experiments on four prevalent few-shot graph classification datasets demonstrate the superiority of FAITH over other state-of-the-art baselines.

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