Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Unifying Generative Model for Graph Learning Algorithms: Label
  Propagation, Graph Convolutions, and Combinations   Semi-supervised learning on graphs is a widely applicable problem in network
science and machine learning. Two standard algorithms -- label propagation and
graph neural networks -- both operate by repeatedly passing information along
edges, the former by passing labels and the latter by passing node features,
modulated by neural networks. These two types of algorithms have largely
developed separately, and there is little understanding about the structure of
network data that would make one of these approaches work particularly well
compared to the other or when the approaches can be meaningfully combined.
Here, we develop a Markov random field model for the data generation process of
node attributes, based on correlations of attributes on and between vertices,
that motivates and unifies these algorithmic approaches. We show that label
propagation, a linearized graph convolutional network, and their combination
can all be derived as conditional expectations under our model, when
conditioning on different attributes. In addition, the data model highlights
deficiencies in existing graph neural networks (while producing new algorithmic
solutions), serves as a rigorous statistical framework for understanding graph
learning issues such as over-smoothing, creates a testbed for evaluating
inductive learning performance, and provides a way to sample graphs attributes
that resemble empirical data. We also find that a new algorithm derived from
our data generation model, which we call a Linear Graph Convolution, performs
extremely well in practice on empirical data, and provide theoretical
justification for why this is the case.

17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con