{"type":"ScholarlyArticle","title":"Subgraph Pooling: Tackling Negative Transfer on Graphs","authors":[{"name":"Zehong Wang","slug":"zehong-wang","given":"Zehong","family":"Wang","affiliation":"University of Notre Dame , Indiana , USA"},{"name":"Zheyuan Zhang","slug":"zheyuan-zhang","given":"Zheyuan","family":"Zhang","affiliation":"University of Notre Dame , Indiana , USA"},{"name":"Chuxu Zhang","slug":"chuxu-zhang","given":"Chuxu","family":"Zhang","affiliation":"Brandeis University , Massachusetts , USA"},{"name":"Yanfang Ye","slug":"yanfang-ye","given":"Yanfang","family":"Ye","affiliation":"University of Notre Dame , Indiana , USA"}],"abstract":"Transfer learning aims to enhance performance on a target task by using knowledge from related tasks. However, when the source and target tasks are not closely aligned, it can lead to reduced performance, known as negative transfer. Unlike in image or text data, we find that negative transfer could commonly occur in graph-structured data, even when source and target graphs have semantic similarities. Specifically, we identify that structural differences significantly amplify the dissimilarities in the node embeddings across graphs. To mitigate this, we bring a new insight in this paper: for semantically similar graphs, although structural differences lead to significant distribution shift in node embeddings, their impact on subgraph embeddings could be marginal. Building on this insight, we introduce Subgraph Pooling (SP) by aggregating nodes sampled from a k-hop neighborhood and Subgraph Pooling++ (SP++) by a random walk, to mitigate the impact of graph structural differences on knowledge transfer. We theoretically analyze the role of SP in reducing graph discrepancy and conduct extensive experiments to evaluate its superiority under various settings. The proposed SP methods are effective yet elegant, which can be easily applied on top of any backbone Graph Neural Networks (GNNs). Our code and data are available at: https://github.com/Zehong-Wang/Subgraph-Pooling.","doi":"10.24963/ijcai.2024/570","url":"https://ijcai.org/proceedings/2024/570","pdf":"https://ijcai.org/proceedings/2024/0570.pdf","markdown":"https://ijcai.org/proceedings/2024/570.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/570","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":570,"pages":{"first":5153,"last":5161},"keywords":["Machine Learning: ML: Sequence and graph learning","Machine Learning: ML: Semi-supervised learning","Machine Learning: ML: Multi-task and transfer learning","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2023] Yuxuan Cao, Jiarong Xu, Carl Yang, Ji- aan Wang, Yunchao Zhang, Chunping Wang, Lei CHEN, and Yang Yang. 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