Affinity-Aware Graph Networks
- URL: http://arxiv.org/abs/2206.11941v1
- Date: Thu, 23 Jun 2022 18:51:35 GMT
- Title: Affinity-Aware Graph Networks
- Authors: Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Veli\v{c}kovi\'c,
Sreenivas Gollapudi
- Abstract summary: Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data.
We explore the use of affinity measures as features in graph neural networks.
We propose message passing networks based on these features and evaluate their performance on a variety of node and graph property prediction tasks.
- Score: 9.888383815189176
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph Neural Networks (GNNs) have emerged as a powerful technique for
learning on relational data. Owing to the relatively limited number of message
passing steps they perform -- and hence a smaller receptive field -- there has
been significant interest in improving their expressivity by incorporating
structural aspects of the underlying graph. In this paper, we explore the use
of affinity measures as features in graph neural networks, in particular
measures arising from random walks, including effective resistance, hitting and
commute times. We propose message passing networks based on these features and
evaluate their performance on a variety of node and graph property prediction
tasks. Our architecture has lower computational complexity, while our features
are invariant to the permutations of the underlying graph. The measures we
compute allow the network to exploit the connectivity properties of the graph,
thereby allowing us to outperform relevant benchmarks for a wide variety of
tasks, often with significantly fewer message passing steps. On one of the
largest publicly available graph regression datasets, OGB-LSC-PCQM4Mv1, we
obtain the best known single-model validation MAE at the time of writing.
Related papers
- TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning [7.879217146851148]
We propose an innovative Graph Neural Network (GNN) architecture that integrates a Top-m attention mechanism aggregation component and a neighborhood aggregation component.
To assess the effectiveness of our proposed model, we have applied it to citation sentiment prediction, a novel task previously unexplored in the GNN field.
arXiv Detail & Related papers (2024-11-23T05:31:25Z) - 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) - TouchUp-G: Improving Feature Representation through Graph-Centric
Finetuning [37.318961625795204]
Graph Neural Networks (GNNs) have become the state-of-the-art approach for many high-impact, real-world graph applications.
For feature-rich graphs, a prevalent practice involves utilizing a PM directly to generate features.
This practice is suboptimal because the node features extracted from PM are graph-agnostic and prevent GNNs from fully utilizing the potential correlations between the graph structure and node features.
arXiv Detail & Related papers (2023-09-25T05:44:40Z) - Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
Modelling long-range dependencies is critical for scene understanding tasks in computer vision.
A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive.
We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
arXiv Detail & Related papers (2022-09-20T14:41:37Z) - GraphMI: Extracting Private Graph Data from Graph Neural Networks [59.05178231559796]
We present textbfGraph textbfModel textbfInversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN.
Specifically, we propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features.
We design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
arXiv Detail & Related papers (2021-06-05T07:07:52Z) - Graph Networks with Spectral Message Passing [1.0742675209112622]
We introduce the Spectral Graph Network, which applies message passing to both the spatial and spectral domains.
Our results show that the Spectral GN promotes efficient training, reaching high performance with fewer training iterations despite having more parameters.
arXiv Detail & Related papers (2020-12-31T21:33:17Z) - 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) - GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework.
We conduct experiments on three graph learning tasks and ten graph datasets.
arXiv Detail & Related papers (2020-06-17T16:18:35Z) - 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) - Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature
Interactions [153.6357310444093]
Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data.
We argue that existing designs of GCN forgo modeling cross features, making GCN less effective for tasks or data where cross features are important.
We design a new operator named Cross-feature Graph Convolution, which explicitly models the arbitrary-order cross features with complexity linear to feature dimension and order size.
arXiv Detail & Related papers (2020-03-05T13:05:27Z) - Graph Representation Learning via Graphical Mutual Information
Maximization [86.32278001019854]
We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations.
We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
arXiv Detail & Related papers (2020-02-04T08:33:49Z)
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.