Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-Local Feature Aggregation on Graphs via Latent Fixed Data Structures   In contrast to image/text data whose order can be used to perform non-local
feature aggregation in a straightforward way using the pooling layers, graphs
lack the tensor representation and mostly the element-wise max/mean function is
utilized to aggregate the locally extracted feature vectors. In this paper, we
present a novel approach for global feature aggregation in Graph Neural
Networks (GNNs) which utilizes a Latent Fixed Data Structure (LFDS) to
aggregate the extracted feature vectors. The locally extracted feature vectors
are sorted/distributed on the LFDS and a latent neural network (CNN/GNN) is
utilized to perform feature aggregation on the LFDS. The proposed approach is
used to design several novel global feature aggregation methods based on the
choice of the LFDS. We introduce multiple LFDSs including loop, 3D tensor
(image), sequence, data driven graphs and an algorithm which sorts/distributes
the extracted local feature vectors on the LFDS. While the computational
complexity of the proposed methods are linear with the order of input graphs,
they achieve competitive or better results.

22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys