Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DAMNETS: A Deep Autoregressive Model for Generating Markovian Network
  Time Series   In this work, we introduce DAMNETS, a deep generative model for Markovian
network time series. Time series of networks are found in many fields such as
trade or payment networks in economics, contact networks in epidemiology or
social media posts over time. Generative models of such data are useful for
Monte-Carlo estimation and data set expansion, which is of interest for both
data privacy and model fitting. Using recent ideas from the Graph Neural
Network (GNN) literature, we introduce a novel GNN encoder-decoder structure in
which an encoder GNN learns a latent representation of the input graph, and a
decoder GNN uses this representation to simulate the network dynamics. We show
using synthetic data sets that DAMNETS can replicate features of network
topology across time observed in the real world, such as changing community
structure and preferential attachment. DAMNETS outperforms competing methods on
all of our measures of sample quality over several real and synthetic data
sets.

9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex