Generative Modeling of Graphs via Joint Diffusion of Node and Edge Attributes

• URL: http://arxiv.org/abs/2402.04046v1
• Date: Tue, 6 Feb 2024 14:48:34 GMT
• Title: Generative Modeling of Graphs via Joint Diffusion of Node and Edge Attributes
• Authors: Nimrod Berman, Eitan Kosman, Dotan Di Castro, Omri Azencot
• Abstract summary: We propose a joint score-based model of nodes and edges for graph generation that considers all graph components. Our approach offers two key novelties: (i) node and edge attributes are combined in an attention module that generates samples based on the two ingredients. We evaluate our method on challenging benchmarks involving real-world and synthetic datasets in which edge features are crucial.
• Score: 16.07858156813397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph generation is integral to various engineering and scientific disciplines. Nevertheless, existing methodologies tend to overlook the generation of edge attributes. However, we identify critical applications where edge attributes are essential, making prior methods potentially unsuitable in such contexts. Moreover, while trivial adaptations are available, empirical investigations reveal their limited efficacy as they do not properly model the interplay among graph components. To address this, we propose a joint score-based model of nodes and edges for graph generation that considers all graph components. Our approach offers two key novelties: (i) node and edge attributes are combined in an attention module that generates samples based on the two ingredients; and (ii) node, edge and adjacency information are mutually dependent during the graph diffusion process. We evaluate our method on challenging benchmarks involving real-world and synthetic datasets in which edge features are crucial. Additionally, we introduce a new synthetic dataset that incorporates edge values. Furthermore, we propose a novel application that greatly benefits from the method due to its nature: the generation of traffic scenes represented as graphs. Our method outperforms other graph generation methods, demonstrating a significant advantage in edge-related measures.

Related papers

• Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation [153.92387500677023]
  We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations. The proposed graph Transformer encoder combines graph convolutions and self-attentions in a Transformer to model both local and global interactions. We also propose a novel self-guided pre-training method for graph representation learning.
  arXiv  Detail & Related papers  (2024-01-15T14:36:38Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)
• SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
  This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations. We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path. Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path. Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
  arXiv  Detail & Related papers  (2021-12-12T14:18:18Z)
• Edge but not Least: Cross-View Graph Pooling [76.71497833616024]
  This paper presents a cross-view graph pooling (Co-Pooling) method to better exploit crucial graph structure information. Through cross-view interaction, edge-view pooling and node-view pooling seamlessly reinforce each other to learn more informative graph-level representations.
  arXiv  Detail & Related papers  (2021-09-24T08:01:23Z)
• Edge Representation Learning with Hypergraphs [36.03482700241067]
  We propose a novel edge representation learning framework based on Dual Hypergraph Transformation (DHT), which transforms the edges of a graph into the nodes of a hypergraph. We validate our edge representation learning method with hypergraphs on diverse graph datasets for graph representation and generation performance. Our edge representation learning and pooling method also largely outperforms state-of-the-art graph pooling methods on graph classification.
  arXiv  Detail & Related papers  (2021-06-30T06:59:05Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Graph Contrastive Learning with Adaptive Augmentation [23.37786673825192]
  We propose a novel graph contrastive representation learning method with adaptive augmentation. Specifically, we design augmentation schemes based on node centrality measures to highlight important connective structures. Our proposed method consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts.
  arXiv  Detail & Related papers  (2020-10-27T15:12:21Z)
• Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution [44.90253939019069]
  We present Coevolved Meta Graph Neural Network (CoMGNN), which applies meta graph attention to heterogeneous graphs with co-evolution of edges and node states. We also propose CoMGNN (ST-CoMGNN) for modelingtemporal patterns on nodes and edges.
  arXiv  Detail & Related papers  (2020-10-09T13:19:39Z)
• Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement [55.2456981313287]
  We propose a new disentanglement enhancement framework for deep generative models for attributed graphs. A novel variational objective is proposed to disentangle the above three types of latent factors, with novel architecture for node and edge deconvolutions. Within each type, individual-factor-wise disentanglement is further enhanced, which is shown to be a generalization of the existing framework for images.
  arXiv  Detail & Related papers  (2020-06-09T16:33:49Z)

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.