Efficient Graph Similarity Computation with Alignment Regularization

• URL: http://arxiv.org/abs/2406.14929v1
• Date: Fri, 21 Jun 2024 07:37:28 GMT
• Title: Efficient Graph Similarity Computation with Alignment Regularization
• Authors: Wei Zhuo, Guang Tan,
• Abstract summary: Graph similarity computation (GSC) is a learning-based prediction task using Graph Neural Networks (GNNs) We show that high-quality learning can be attained with a simple yet powerful regularization technique, which we call the Alignment Regularization (AReg) In the inference stage, the graph-level representations learned by the GNN encoder are directly used to compute the similarity score without using AReg again to speed up inference.
• Score: 7.143879014059894
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the graph similarity computation (GSC) task based on graph edit distance (GED) estimation. State-of-the-art methods treat GSC as a learning-based prediction task using Graph Neural Networks (GNNs). To capture fine-grained interactions between pair-wise graphs, these methods mostly contain a node-level matching module in the end-to-end learning pipeline, which causes high computational costs in both the training and inference stages. We show that the expensive node-to-node matching module is not necessary for GSC, and high-quality learning can be attained with a simple yet powerful regularization technique, which we call the Alignment Regularization (AReg). In the training stage, the AReg term imposes a node-graph correspondence constraint on the GNN encoder. In the inference stage, the graph-level representations learned by the GNN encoder are directly used to compute the similarity score without using AReg again to speed up inference. We further propose a multi-scale GED discriminator to enhance the expressive ability of the learned representations. Extensive experiments on real-world datasets demonstrate the effectiveness, efficiency and transferability of our approach.

Related papers

• LASE: Learned Adjacency Spectral Embeddings [7.612218105739107]
  We learn nodal Adjacency Spectral Embeddings (ASE) from graph inputs. LASE is interpretable, parameter efficient, robust to inputs with unobserved edges. LASE layers combine Graph Convolutional Network (GCN) and fully-connected Graph Attention Network (GAT) modules.
  arXiv  Detail & Related papers  (2024-12-23T17:35:19Z)
• FIT-GNN: Faster Inference Time for GNNs Using Coarsening [1.323700980948722]
  coarsening-based methods are used to reduce the graph into a smaller graph, resulting in faster computation. Prior research has not adequately addressed the computational costs during the inference phase. This paper presents a novel approach to improve the scalability of GNNs by reducing computational burden during both training and inference phases.
  arXiv  Detail & Related papers  (2024-10-19T06:27:24Z)
• T-GAE: Transferable Graph Autoencoder for Network Alignment [79.89704126746204]
  T-GAE is a graph autoencoder framework that leverages transferability and stability of GNNs to achieve efficient network alignment without retraining. Our experiments demonstrate that T-GAE outperforms the state-of-the-art optimization method and the best GNN approach by up to 38.7% and 50.8%, respectively.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [70.51126383984555]
  We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes. The efficient computation is enabled by a kernerlized Gumbel-Softmax operator. Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
  arXiv  Detail & Related papers  (2023-06-14T09:21:15Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Training Free Graph Neural Networks for Graph Matching [103.45755859119035]
  TFGM is a framework to boost the performance of Graph Neural Networks (GNNs) based graph matching without training. Applying TFGM on various GNNs shows promising improvements over baselines.
  arXiv  Detail & Related papers  (2022-01-14T09:04:46Z)
• Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning [63.97983530843762]
  Graph Neural Networks (GNNs) have achieved great success in graph representation learning. GNNs generate identical representations for graph substructures that may in fact be very different. More powerful GNNs, proposed recently by mimicking higher-order tests, are inefficient as they cannot sparsity of underlying graph structure. We propose Distance Depiction (DE) as a new class of graph representation learning.
  arXiv  Detail & Related papers  (2020-08-31T23:15:40Z)
• Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification [70.50751397870972]
  FastGAT is a method to make attention based GNNs lightweight by using spectral sparsification to generate an optimal pruning of the input graph. We experimentally evaluate FastGAT on several large real world graph datasets for node classification tasks.
  arXiv  Detail & Related papers  (2020-06-15T22:07:54Z)
• Self-Constructing Graph Convolutional Networks for Semantic Labeling [23.623276007011373]
  We propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings. SCG can automatically obtain optimized non-local context graphs from complex-shaped objects in aerial imagery. We demonstrate the effectiveness and flexibility of the proposed SCG on the publicly available ISPRS Vaihingen dataset.
  arXiv  Detail & Related papers  (2020-03-15T21:55:24Z)

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.