Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How Neural Processes Improve Graph Link Prediction   Link prediction is a fundamental problem in graph data analysis. While most
of the literature focuses on transductive link prediction that requires all the
graph nodes and majority of links in training, inductive link prediction, which
only uses a proportion of the nodes and their links in training, is a more
challenging problem in various real-world applications. In this paper, we
propose a meta-learning approach with graph neural networks for link
prediction: Neural Processes for Graph Neural Networks (NPGNN), which can
perform both transductive and inductive learning tasks and adapt to patterns in
a large new graph after training with a small subgraph. Experiments on
real-world graphs are conducted to validate our model, where the results
suggest that the proposed method achieves stronger performance compared to
other state-of-the-art models, and meanwhile generalizes well when training on
a small subgraph.

2
The categories of this paper are: math
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn