Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Few-Shot Learning on Graphs: A Survey   Graph representation learning has attracted tremendous attention due to its
remarkable performance in many real-world applications. However, prevailing
(semi-)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.

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The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph