{"type":"ScholarlyArticle","title":"Few-Shot Learning on Graphs","authors":[{"name":"Chuxu Zhang","slug":"chuxu-zhang","given":"Chuxu","family":"Zhang","affiliation":"Brandeis University , Waltham , MA , USA"},{"name":"Kaize Ding","slug":"kaize-ding","given":"Kaize","family":"Ding","affiliation":"Arizona State University , Tempe , Arizona , USA"},{"name":"Jundong Li","slug":"jundong-li","given":"Jundong","family":"Li","affiliation":"University of Virginia , Charlottesville , Virginia , USA"},{"name":"Xiangliang Zhang","slug":"xiangliang-zhang","given":"Xiangliang","family":"Zhang","affiliation":"University of Notre Dame , Notre Dame , Indiana , USA"},{"name":"Yanfang Ye","slug":"yanfang-ye","given":"Yanfang","family":"Ye","affiliation":"University of Notre Dame , Notre Dame , Indiana , USA"},{"name":"Nitesh V. Chawla","slug":"nitesh-v-chawla","given":"Nitesh V.","family":"Chawla","affiliation":"University of Notre Dame , Notre Dame , Indiana , USA"},{"name":"Huan Liu","slug":"huan-liu","given":"Huan","family":"Liu","affiliation":"Arizona State University , Tempe , Arizona , USA"}],"abstract":"Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling is always time and resource consuming. In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data  challenge. There have been many studies working on FSLG recently. In this paper, we comprehensively survey these work in the form of a series of methods and applications. Specifically, we first introduce FSLG challenges and bases, then categorize and summarize existing work of FSLG in terms of three major graph mining tasks at different granularity levels, i.e., node, edge, and graph. Finally, we share our thoughts on some future research directions of FSLG. The authors of this survey have contributed significantly to the AI literature on FSLG over the last few years.","doi":"10.24963/ijcai.2022/789","url":"https://ijcai.org/proceedings/2022/789","pdf":"https://ijcai.org/proceedings/2022/0789.pdf","markdown":"https://ijcai.org/proceedings/2022/789.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/789","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":"Survey Track","track":"Survey Track","number":789,"pages":{"first":5662,"last":5669},"keywords":["Survey Track: Machine Learning","Survey Track: Data Mining"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987307","length_sec":85,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985450","length_sec":335,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Baek et al., 2020] Jinheon Baek, Dong Bok Lee, and Sung Ju Hwang. 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