Generalized Graph Transformer Variational Autoencoder

• URL: http://arxiv.org/abs/2512.00612v1
• Date: Sat, 29 Nov 2025 19:53:44 GMT
• Title: Generalized Graph Transformer Variational Autoencoder
• Authors: Siddhant Karki,
• Abstract summary: We propose the Generalized Graph Transformer Variational Autoencoder (GGT-VAE)<n>Our model integrates Generalized Graph Transformer Architecture with Variational Autoencoder framework for link prediction.<n> Experimental results on several benchmark datasets demonstrate that GGT-VAE consistently achieves above-baseline performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph link prediction has long been a central problem in graph representation learning in both network analysis and generative modeling. Recent progress in deep learning has introduced increasingly sophisticated architectures for capturing relational dependencies within graph-structured data. In this work, we propose the Generalized Graph Transformer Variational Autoencoder (GGT-VAE). Our model integrates Generalized Graph Transformer Architecture with Variational Autoencoder framework for link prediction. Unlike prior GraphVAE, GCN, or GNN approaches, GGT-VAE leverages transformer style global self-attention mechanism along with laplacian positional encoding to model structural patterns across nodes into a latent space without relying on message passing. Experimental results on several benchmark datasets demonstrate that GGT-VAE consistently achieves above-baseline performance in terms of ROC-AUC and Average Precision. To the best of our knowledge, this is among the first studies to explore graph structure generation using a generalized graph transformer backbone in a variational framework.

Related papers

• Relational Graph Transformer [44.56132732108148]
  Graph Transformer (Relel) is the first graph transformer designed specifically for relational tables.<n>Relel employs a novel multi-element tokenization strategy that decomposes each node into five components.<n>Our architecture combines local attention over sampled subgraphs with global attention to learnable centroids.
  arXiv  Detail & Related papers  (2025-05-16T07:51:58Z)
• Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks [50.42343781348247]
  We develop a graph Poisson factor analysis (GPFA) which provides analytic conditional posteriors to improve the inference accuracy. We also extend GPFA to a multi-stochastic-layer version named graph Poisson gamma belief network (GPGBN) to capture the hierarchical document relationships at multiple semantic levels. Our models can extract high-quality hierarchical latent document representations and achieve promising performance on various graph analytic tasks.
  arXiv  Detail & Related papers  (2024-10-13T02:22:14Z)
• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Automatic Graph Topology-Aware Transformer [50.2807041149784]
  We build a comprehensive graph Transformer search space with the micro-level and macro-level designs. EGTAS evolves graph Transformer topologies at the macro level and graph-aware strategies at the micro level. We demonstrate the efficacy of EGTAS across a range of graph-level and node-level tasks.
  arXiv  Detail & Related papers  (2024-05-30T07:44:31Z)
• Graph Transformers without Positional Encodings [0.7252027234425334]
  We introduce Eigenformer, a Graph Transformer employing a novel spectrum-aware attention mechanism cognizant of the Laplacian spectrum of the graph. We empirically show that it achieves performance competetive with SOTA Graph Transformers on a number of standard GNN benchmarks.
  arXiv  Detail & Related papers  (2024-01-31T12:33:31Z)
• Discrete Graph Auto-Encoder [52.50288418639075]
  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.
  arXiv  Detail & Related papers  (2023-06-13T12:40:39Z)
• Transformers over Directed Acyclic Graphs [6.263470141349622]
  We study transformers over directed acyclic graphs (DAGs) and propose architecture adaptations tailored to DAGs. We show that it is effective in making graph transformers generally outperform graph neural networks tailored to DAGs and in improving SOTA graph transformer performance in terms of both quality and efficiency.
  arXiv  Detail & Related papers  (2022-10-24T12:04:52Z)
• Deformable Graph Transformer [31.254872949603982]
  We propose Deformable Graph Transformer (DGT) that performs sparse attention with dynamically sampled key and value pairs. Experiments demonstrate that our novel graph Transformer consistently outperforms existing Transformer-based models.
  arXiv  Detail & Related papers  (2022-06-29T00:23:25Z)
• Transformer for Graphs: An Overview from Architecture Perspective [86.3545861392215]
  It's imperative to sort out the existing Transformer models for graphs and systematically investigate their effectiveness on various graph tasks. We first disassemble the existing models and conclude three typical ways to incorporate the graph information into the vanilla Transformer. Our experiments confirm the benefits of current graph-specific modules on Transformer and reveal their advantages on different kinds of graph tasks.
  arXiv  Detail & Related papers  (2022-02-17T06:02:06Z)
• Rethinking Graph Transformers with Spectral Attention [13.068288784805901]
  We present the $textitSpectral Attention Network$ (SAN), which uses a learned positional encoding (LPE) to learn the position of each node in a given graph. By leveraging the full spectrum of the Laplacian, our model is theoretically powerful in distinguishing graphs, and can better detect similar sub-structures from their resonance. Our model performs on par or better than state-of-the-art GNNs, and outperforms any attention-based model by a wide margin.
  arXiv  Detail & Related papers  (2021-06-07T18:11:11Z)
• Heterogeneous Graph Transformer [49.675064816860505]
  Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs. To handle dynamic heterogeneous graphs, we introduce the relative temporal encoding technique into HGT. To handle Web-scale graph data, we design the heterogeneous mini-batch graph sampling algorithm---HGSampling---for efficient and scalable training.
  arXiv  Detail & Related papers  (2020-03-03T04:49:21Z)
• Adaptive Graph Auto-Encoder for General Data Clustering [90.8576971748142]
  Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. We propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs.
  arXiv  Detail & Related papers  (2020-02-20T10:11:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.