Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Memory-Based Graph Networks   Graph neural networks (GNNs) are a class of deep models that operate on data
with arbitrary topology represented as graphs. We introduce an efficient memory
layer for GNNs that can jointly learn node representations and coarsen the
graph. We also introduce two new networks based on this layer: memory-based GNN
(MemGNN) and graph memory network (GMN) that can learn hierarchical graph
representations. The experimental results shows that the proposed models
achieve state-of-the-art results in eight out of nine graph classification and
regression benchmarks. We also show that the learned representations could
correspond to chemical features in the molecule data. Code and reference
implementations are released at: https://github.com/amirkhas/GraphMemoryNet

17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph