{"type":"ScholarlyArticle","title":"FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs","authors":[{"name":"Song Wang","slug":"song-wang","given":"Song","family":"Wang","affiliation":"University of Virginia"},{"name":"Yushun Dong","slug":"yushun-dong","given":"Yushun","family":"Dong","affiliation":"University of Virginia"},{"name":"Xiao Huang","slug":"xiao-huang","given":"Xiao","family":"Huang","affiliation":"Hong Kong Polytechnic University"},{"name":"Chen Chen","slug":"chen-chen","given":"Chen","family":"Chen","affiliation":"University of Virginia"},{"name":"Jundong Li","slug":"jundong-li","given":"Jundong","family":"Li","affiliation":"University of Virginia"}],"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.","doi":"10.24963/ijcai.2022/317","url":"https://ijcai.org/proceedings/2022/317","pdf":"https://ijcai.org/proceedings/2022/0317.pdf","markdown":"https://ijcai.org/proceedings/2022/317.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/317","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":317,"pages":{"first":2284,"last":2290},"keywords":["Machine Learning: Classification","Machine Learning: Representation learning","Machine Learning: Meta-Learning","Data Mining: Mining Graphs"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986660","length_sec":76,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984803","length_sec":345,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bai et al., 2019] Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, and Wei Wang. 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