Discrete Graph Auto-Encoder
- URL: http://arxiv.org/abs/2306.07735v2
- Date: Tue, 30 Jan 2024 14:46:57 GMT
- Title: Discrete Graph Auto-Encoder
- Authors: Yoann Boget, Magda Gregorova, Alexandros Kalousis
- Abstract summary: We introduce a new framework named Discrete Graph Auto-Encoder (DGAE)
We first use a permutation-equivariant auto-encoder to convert graphs into sets of discrete latent node representations.
In the second step, we sort the sets of discrete latent representations and learn their distribution with a specifically designed auto-regressive model.
- Score: 52.50288418639075
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Despite advances in generative methods, accurately modeling the distribution
of graphs remains a challenging task primarily because of the absence of
predefined or inherent unique graph representation. Two main strategies have
emerged to tackle this issue: 1) restricting the number of possible
representations by sorting the nodes, or 2) using
permutation-invariant/equivariant functions, specifically Graph Neural Networks
(GNNs).
In this paper, we introduce a new framework named Discrete Graph Auto-Encoder
(DGAE), which leverages the strengths of both strategies and mitigate their
respective limitations. In essence, we propose a strategy in 2 steps. We first
use a permutation-equivariant auto-encoder to convert graphs into sets of
discrete latent node representations, each node being represented by a sequence
of quantized vectors. In the second step, we sort the sets of discrete latent
representations and learn their distribution with a specifically designed
auto-regressive model based on the Transformer architecture.
Through multiple experimental evaluations, we demonstrate the competitive
performances of our model in comparison to the existing state-of-the-art across
various datasets. Various ablation studies support the interest of our method.
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