Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation

• URL: http://arxiv.org/abs/2106.06189v2
• Date: Mon, 14 Jun 2021 09:51:52 GMT
• Title: Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
• Authors: Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Liping Liu
• Abstract summary: A graph generative model defines a distribution over graphs. We derive the exact joint probability over the graph and the node ordering of the sequential process. We train graph generative models by maximizing this bound, without using the ad-hoc node orderings of previous methods.
• Score: 18.03898476141173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A graph generative model defines a distribution over graphs. One type of generative model is constructed by autoregressive neural networks, which sequentially add nodes and edges to generate a graph. However, the likelihood of a graph under the autoregressive model is intractable, as there are numerous sequences leading to the given graph; this makes maximum likelihood estimation challenging. Instead, in this work we derive the exact joint probability over the graph and the node ordering of the sequential process. From the joint, we approximately marginalize out the node orderings and compute a lower bound on the log-likelihood using variational inference. We train graph generative models by maximizing this bound, without using the ad-hoc node orderings of previous methods. Our experiments show that the log-likelihood bound is significantly tighter than the bound of previous schemes. Moreover, the models fitted with the proposed algorithm can generate high-quality graphs that match the structures of target graphs not seen during training. We have made our code publicly available at \hyperref[https://github.com/tufts-ml/graph-generation-vi]{https://github.com/tufts-ml/graph-generation-vi}.

Related papers

• IFH: a Diffusion Framework for Flexible Design of Graph Generative Models [53.219279193440734]
  Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successive additions of nodes and edges. This paper proposes a graph generative model, called Insert-Fill-Halt (IFH), that supports the specification of a sequentiality degree.
  arXiv  Detail & Related papers  (2024-08-23T16:24:40Z)
• Overcoming Order in Autoregressive Graph Generation [12.351817671944515]
  Graph generation is a fundamental problem in various domains, including chemistry and social networks. Recent work has shown that molecular graph generation using recurrent neural networks (RNNs) is advantageous compared to traditional generative approaches.
  arXiv  Detail & Related papers  (2024-02-04T09:58:22Z)
• Autoregressive Diffusion Model for Graph Generation [12.390149720274904]
  We propose an emphautoregressive diffusion model for graph generation. Unlike existing methods, we define a node-absorbing diffusion process that operates directly in the discrete graph space. Our experiments on six diverse generic graph datasets and two molecule datasets show that our model achieves better or comparable generation performance with previous state-of-the-art.
  arXiv  Detail & Related papers  (2023-07-17T21:21:18Z)
• HiGen: Hierarchical Graph Generative Networks [2.3931689873603603]
  Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods. We propose a novel graph generative network that captures the hierarchical nature of graphs and successively generates the graph sub-structures in a coarse-to-fine fashion. This modular approach enables scalable graph generation for large and complex graphs.
  arXiv  Detail & Related papers  (2023-05-30T18:04:12Z)
• Graph Generation with Diffusion Mixture [57.78958552860948]
  Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. We propose a generative framework that models the topology of graphs by explicitly learning the final graph structures of the diffusion process.
  arXiv  Detail & Related papers  (2023-02-07T17:07:46Z)
• GrannGAN: Graph annotation generative adversarial networks [72.66289932625742]
  We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases. In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features.
  arXiv  Detail & Related papers  (2022-12-01T11:49:07Z)
• Node Copying: A Random Graph Model for Effective Graph Sampling [35.957719744856696]
  We introduce the node copying model for constructing a distribution over graphs. We show the usefulness of the copying model in three tasks. We employ our proposed model to mitigate the effect of adversarial attacks on the graph topology.
  arXiv  Detail & Related papers  (2022-08-04T04:04:49Z)
• Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning [84.35102534158621]
  We study pre-trained language models that generate explanation graphs in an end-to-end manner. We propose simple yet effective ways of graph perturbations via node and edge edit operations. Our methods lead to significant improvements in both structural and semantic accuracy of explanation graphs.
  arXiv  Detail & Related papers  (2022-04-11T00:58:27Z)
• Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks [0.6236890292833384]
  In this paper, we propose a novel algorithm called Bayesian Graph Convolutional Network using Neighborhood Random Walk Sampling (BGCN-NRWS) BGCN-NRWS uses a Markov Chain Monte Carlo (MCMC) based graph sampling algorithm utilizing graph structure, reduces overfitting by using a variational inference layer, and yields consistently competitive classification results compared to the state-of-the-art in semi-supervised node classification.
  arXiv  Detail & Related papers  (2021-12-14T20:58:27Z)
• Line Graph Neural Networks for Link Prediction [71.00689542259052]
  We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. In this formalism, a link prediction problem is converted to a graph classification task. We propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
  arXiv  Detail & Related papers  (2020-10-20T05:54:31Z)
• Permutation Invariant Graph Generation via Score-Based Generative Modeling [114.12935776726606]
  We propose a permutation invariant approach to modeling graphs, using the recent framework of score-based generative modeling. In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph. For graph generation, we find that our learning approach achieves better or comparable results to existing models on benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-02T03:06:14Z)

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.