Network Alignment with Transferable Graph Autoencoders

• URL: http://arxiv.org/abs/2310.03272v3
• Date: Wed, 22 May 2024 04:14:39 GMT
• Title: Network Alignment with Transferable Graph Autoencoders
• Authors: Jiashu He, Charilaos I. Kanatsoulis, Alejandro Ribeiro,
• Abstract summary: We propose a novel graph autoencoder architecture designed to extract powerful and robust node embeddings. We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs. Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining.
• Score: 79.89704126746204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network alignment is the task of establishing one-to-one correspondences between the nodes of different graphs and finds a plethora of applications in high-impact domains. However, this task is known to be NP-hard in its general form, and existing algorithms do not scale up as the size of the graphs increases. To tackle both challenges we propose a novel generalized graph autoencoder architecture, designed to extract powerful and robust node embeddings, that are tailored to the alignment task. We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs and can achieve more accurate alignment compared to classical spectral methods. Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining. Extensive experiments on both network and sub-network alignment with real-world graphs provide corroborating evidence supporting the effectiveness and scalability of the proposed approach.

Related papers

• Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach [12.451324619122405]
  This paper introduces a method to extract a smaller, task-focused subgraph that maintains key information while reducing communication overhead. Our approach utilizes graph neural networks (GNNs) and the graph information bottleneck (GIB) principle to create a compact, informative, and robust graph representation suitable for transmission.
  arXiv  Detail & Related papers  (2024-09-04T14:01:56Z)
• Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks [15.36505600407192]
  We propose a learning-based approach that efficiently uncovers graph topologies with global convergence guarantees. We demonstrate the effectiveness of our approach in identifying graphs in multi-robot formation and flocking tasks.
  arXiv  Detail & Related papers  (2023-07-10T07:09:12Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Meta Propagation Networks for Graph Few-shot Semi-supervised Learning [39.96930762034581]
  We propose a novel network architecture equipped with a novel meta-learning algorithm to solve this problem. In essence, our framework Meta-PN infers high-quality pseudo labels on unlabeled nodes via a meta-learned label propagation strategy. Our approach offers easy and substantial performance gains compared to existing techniques on various benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-18T00:11:56Z)
• Spectral Graph Convolutional Networks With Lifting-based Adaptive Graph Wavelets [81.63035727821145]
  Spectral graph convolutional networks (SGCNs) have been attracting increasing attention in graph representation learning. We propose a novel class of spectral graph convolutional networks that implement graph convolutions with adaptive graph wavelets.
  arXiv  Detail & Related papers  (2021-08-03T17:57:53Z)
• 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)
• Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs [17.823543937167848]
  mGCMN is a novel framework which utilizes node feature information and the higher order local structure of the graph. It will greatly improve the learning efficiency of the graph neural network and promote a brand-new learning mode establishment.
  arXiv  Detail & Related papers  (2020-07-31T04:18:20Z)
• Progressive Graph Convolutional Networks for Semi-Supervised Node Classification [97.14064057840089]
  Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. We propose a method to automatically build compact and task-specific graph convolutional networks.
  arXiv  Detail & Related papers  (2020-03-27T08:32:16Z)
• An Uncoupled Training Architecture for Large Graph Learning [20.784230322205232]
  We present Node2Grids, a flexible uncoupled training framework for embedding graph data into grid-like data. By ranking each node's influence through degree, Node2Grids selects the most influential first-order as well as second-order neighbors with central node fusion information. For further improving the efficiency of downstream tasks, a simple CNN-based neural network is employed to capture the significant information from the mapped grid-like data.
  arXiv  Detail & Related papers  (2020-03-21T11:49:16Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)

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.