Will More Expressive Graph Neural Networks do Better on Generative Tasks?

• URL: http://arxiv.org/abs/2308.11978v4
• Date: Tue, 20 Feb 2024 13:56:41 GMT
• Title: Will More Expressive Graph Neural Networks do Better on Generative Tasks?
• Authors: Xiandong Zou, Xiangyu Zhao, Pietro Li\`o, Yiren Zhao
• Abstract summary: Graph Neural Network (GNN) architectures are often underexplored. We replace the underlying GNNs of graph generative models with more expressive GNNs. advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches.
• Score: 27.412913421460388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph generation poses a significant challenge as it involves predicting a complete graph with multiple nodes and edges based on simply a given label. This task also carries fundamental importance to numerous real-world applications, including de-novo drug and molecular design. In recent years, several successful methods have emerged in the field of graph generation. However, these approaches suffer from two significant shortcomings: (1) the underlying Graph Neural Network (GNN) architectures used in these methods are often underexplored; and (2) these methods are often evaluated on only a limited number of metrics. To fill this gap, we investigate the expressiveness of GNNs under the context of the molecular graph generation task, by replacing the underlying GNNs of graph generative models with more expressive GNNs. Specifically, we analyse the per- formance of six GNNs in two different generative frameworks -- autoregressive generation models, such as GCPN and GraphAF, and one-shot generation models, such as GraphEBM -- on six different molecular generative objectives on the ZINC-250k dataset. Through our extensive experiments, we demonstrate that advanced GNNs can indeed improve the performance of GCPN, GraphAF, and GraphEBM on molecular generation tasks, but GNN expressiveness is not a necessary condition for a good GNN-based generative model. Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are impor- tant metrics for de-novo molecular design.

Related papers

• Spectral Greedy Coresets for Graph Neural Networks [61.24300262316091]
  The ubiquity of large-scale graphs in node-classification tasks hinders the real-world applications of Graph Neural Networks (GNNs) This paper studies graph coresets for GNNs and avoids the interdependence issue by selecting ego-graphs based on their spectral embeddings. Our spectral greedy graph coreset (SGGC) scales to graphs with millions of nodes, obviates the need for model pre-training, and applies to low-homophily graphs.
  arXiv  Detail & Related papers  (2024-05-27T17:52:12Z)
• First-order PDES for Graph Neural Networks: Advection And Burgers Equation Models [1.4174475093445238]
  This paper presents new Graph Neural Network models that incorporate two first-order Partial Differential Equations (PDEs) Our experimental findings highlight the capacity of our new PDE model to achieve comparable results with higher-order PDE models and fix the over-smoothing problem up to 64 layers. Results underscore the adaptability and versatility of GNNs, indicating that unconventional approaches can yield outcomes on par with established techniques.
  arXiv  Detail & Related papers  (2024-04-03T21:47:02Z)
• Edge Directionality Improves Learning on Heterophilic Graphs [42.5099159786891]
  We introduce Directed Graph Neural Network (Dir-GNN), a novel framework for deep learning on directed graphs. Dir-GNN can be used to extend any Message Passing Neural Network (MPNN) to account for edge directionality information. We prove that Dir-GNN matches the expressivity of the Directed Weisfeiler-Lehman test, exceeding that of conventional MPNNs.
  arXiv  Detail & Related papers  (2023-05-17T18:06:43Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• Graph Generative Model for Benchmarking Graph Neural Networks [73.11514658000547]
  We introduce a novel graph generative model that learns and reproduces the distribution of real-world graphs in a privacy-controlled way. Our model can successfully generate privacy-controlled, synthetic substitutes of large-scale real-world graphs that can be effectively used to benchmark GNN models.
  arXiv  Detail & Related papers  (2022-07-10T06:42:02Z)
• Image-Like Graph Representations for Improved Molecular Property Prediction [7.119677737397071]
  We propose a new intrinsic molecular representation that bypasses the need for GNNs entirely, dubbed CubeMol. Our fixed-dimensional representation, when paired with a transformer model, exceeds the performance of state-of-the-art GNN models and provides a path for scalability.
  arXiv  Detail & Related papers  (2021-11-20T22:39:11Z)
• A Unified View on Graph Neural Networks as Graph Signal Denoising [49.980783124401555]
  Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models can be regarded as solving a graph denoising problem. We instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes.
  arXiv  Detail & Related papers  (2020-10-05T04:57:18Z)
• Hierarchical Message-Passing Graph Neural Networks [12.207978823927386]
  We propose a novel Hierarchical Message-passing Graph Neural Networks framework. Key idea is generating a hierarchical structure that re-organises all nodes in a flat graph into multi-level super graphs. We present the first model to implement this framework, termed Hierarchical Community-aware Graph Neural Network (HC-GNN)
  arXiv  Detail & Related papers  (2020-09-08T13:11:07Z)
• GPT-GNN: Generative Pre-Training of Graph Neural Networks [93.35945182085948]
  Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. We present the GPT-GNN framework to initialize GNNs by generative pre-training. We show that GPT-GNN significantly outperforms state-of-the-art GNN models without pre-training by up to 9.1% across various downstream tasks.
  arXiv  Detail & Related papers  (2020-06-27T20:12:33Z)
• XGNN: Towards Model-Level Explanations of Graph Neural Networks [113.51160387804484]
  Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information. GNNs are mostly treated as black-boxes and lack human intelligible explanations. We propose a novel approach, known as XGNN, to interpret GNNs at the model-level.
  arXiv  Detail & Related papers  (2020-06-03T23:52:43Z)

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.