Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Information Diffusion in Time-Varying Graphs through Deep
  Reinforcement Learning   Network seeding for efficient information diffusion over time-varying
graphs~(TVGs) is a challenging task with many real-world applications. There
are several ways to model this spatio-temporal influence maximization problem,
but the ultimate goal is to determine the best moment for a node to start the
diffusion process. In this context, we propose Spatio-Temporal Influence
Maximization~(STIM), a model trained with Reinforcement Learning and Graph
Embedding over a set of artificial TVGs that is capable of learning the
temporal behavior and connectivity pattern of each node, allowing it to predict
the best moment to start a diffusion through the TVG. We also develop a special
set of artificial TVGs used for training that simulate a stochastic diffusion
process in TVGs, showing that the STIM network can learn an efficient policy
even over a non-deterministic environment. STIM is also evaluated with a
real-world TVG, where it also manages to efficiently propagate information
through the nodes. Finally, we also show that the STIM model has a time
complexity of $O(|E|)$. STIM, therefore, presents a novel approach for
efficient information diffusion in TVGs, being highly versatile, where one can
change the goal of the model by simply changing the adopted reward function.

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The categories of this paper are: eess
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The categories of this paper are: econ
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org