Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Topological Graph Neural Networks   Graph neural networks (GNNs) are a powerful architecture for tackling graph
learning tasks, yet have been shown to be oblivious to eminent substructures
such as cycles. We present TOGL, a novel layer that incorporates global
topological information of a graph using persistent homology. TOGL can be
easily integrated into any type of GNN and is strictly more expressive (in
terms the Weisfeiler--Lehman graph isomorphism test) than message-passing GNNs.
Augmenting GNNs with TOGL leads to improved predictive performance for graph
and node classification tasks, both on synthetic data sets, which can be
classified by humans using their topology but not by ordinary GNNs, and on
real-world data.

28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph