Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Latent Network Embedding via Adversarial Auto-encoders   Graph auto-encoders have proved to be useful in network embedding task.
However, current models only consider explicit structures and fail to explore
the informative latent structures cohered in networks. To address this issue,
we propose a latent network embedding model based on adversarial graph
auto-encoders. Under this framework, the problem of discovering latent
structures is formulated as inferring the latent ties from partial
observations. A latent transmission matrix that describes the strengths of
existing edges and latent ties is derived based on influence cascades sampled
by simulating diffusion processes over networks. Besides, since the inference
process may bring extra noises, we introduce an adversarial training that works
as regularization to dislodge noises and improve the model robustness.
Extensive experiments on link prediction and node classification tasks show
that the proposed model achieves superior results compared with baseline
models.

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The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin