Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Overcoming Catastrophic Forgetting in Graph Neural Networks with
  Experience Replay   Graph Neural Networks (GNNs) have recently received significant research
attention due to their superior performance on a variety of graph-related
learning tasks. Most of the current works focus on either static or dynamic
graph settings, addressing a single particular task, e.g., node/graph
classification, link prediction. In this work, we investigate the question: can
GNNs be applied to continuously learning a sequence of tasks? Towards that, we
explore the Continual Graph Learning (CGL) paradigm and present the Experience
Replay based framework ER-GNN for CGL to alleviate the catastrophic forgetting
problem in existing GNNs. ER-GNN stores knowledge from previous tasks as
experiences and replays them when learning new tasks to mitigate the
catastrophic forgetting issue. We propose three experience node selection
strategies: mean of feature, coverage maximization, and influence maximization,
to guide the process of selecting experience nodes. Extensive experiments on
three benchmark datasets demonstrate the effectiveness of our ER-GNN and shed
light on the incremental graph (non-Euclidean) structure learning.

31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess