Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Continuous Graph Flow   In this paper, we propose Continuous Graph Flow, a generative continuous flow
based method that aims to model complex distributions of graph-structured data.
Once learned, the model can be applied to an arbitrary graph, defining a
probability density over the random variables represented by the graph. It is
formulated as an ordinary differential equation system with shared and reusable
functions that operate over the graphs. This leads to a new type of neural
graph message passing scheme that performs continuous message passing over
time. This class of models offers several advantages: a flexible representation
that can generalize to variable data dimensions; ability to model dependencies
in complex data distributions; reversible and memory-efficient; and exact and
efficient computation of the likelihood of the data. We demonstrate the
effectiveness of our model on a diverse set of generation tasks across
different domains: graph generation, image puzzle generation, and layout
generation from scene graphs. Our proposed model achieves significantly better
performance compared to state-of-the-art models.

18
The categories of this paper are: econ
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat