Strongly Topology-preserving GNNs for Brain Graph Super-resolution

• URL: http://arxiv.org/abs/2411.02525v1
• Date: Fri, 01 Nov 2024 03:29:04 GMT
• Title: Strongly Topology-preserving GNNs for Brain Graph Super-resolution
• Authors: Pragya Singh, Islem Rekik,
• Abstract summary: Brain graph super-resolution (SR) is an under-explored yet highly relevant task in network neuroscience. Current SR methods leverage graph neural networks (GNNs) thanks to their ability to handle graph-structured datasets. We develop an efficient mapping from the edge space of our low-resolution (LR) brain graphs to the node space of a high-resolution (HR) dual graph.
• Score: 5.563171090433323
• License:
• Abstract: Brain graph super-resolution (SR) is an under-explored yet highly relevant task in network neuroscience. It circumvents the need for costly and time-consuming medical imaging data collection, preparation, and processing. Current SR methods leverage graph neural networks (GNNs) thanks to their ability to natively handle graph-structured datasets. However, most GNNs perform node feature learning, which presents two significant limitations: (1) they require computationally expensive methods to learn complex node features capable of inferring connectivity strength or edge features, which do not scale to larger graphs; and (2) computations in the node space fail to adequately capture higher-order brain topologies such as cliques and hubs. However, numerous studies have shown that brain graph topology is crucial in identifying the onset and presence of various neurodegenerative disorders like Alzheimer and Parkinson. Motivated by these challenges and applications, we propose our STP-GSR framework. It is the first graph SR architecture to perform representation learning in higher-order topological space. Specifically, using the primal-dual graph formulation from graph theory, we develop an efficient mapping from the edge space of our low-resolution (LR) brain graphs to the node space of a high-resolution (HR) dual graph. This approach ensures that node-level computations on this dual graph correspond naturally to edge-level learning on our HR brain graphs, thereby enforcing strong topological consistency within our framework. Additionally, our framework is GNN layer agnostic and can easily learn from smaller, scalable GNNs, reducing computational requirements. We comprehensively benchmark our framework across seven key topological measures and observe that it significantly outperforms the previous state-of-the-art methods and baselines.

Related papers

• Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders [27.280927277680515]
  We propose a topology-aware graph augmentation (TGA) framework, comprising a pretext model to train a generalizable encoder and a task-specific model to perform downstream tasks. Experiments on 1, 688 fMRI scans suggest that TGA outperforms several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-31T19:37:20Z)
• 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)
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [124.21408098724551]
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs) We present a new ensembling training manner, named EnGCN, to address the existing issues. Our proposed method has achieved new state-of-the-art (SOTA) performance on large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-14T03:43:05Z)
• Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks [52.566735716983956]
  We propose a graph gradual pruning framework termed CGP to dynamically prune GNNs. Unlike LTH-based methods, the proposed CGP approach requires no re-training, which significantly reduces the computation costs. Our proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of existing methods.
  arXiv  Detail & Related papers  (2022-07-18T14:23:31Z)
• ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network [72.16255675586089]
  We propose an Adaptive Curvature Exploration Hyperbolic Graph NeuralNetwork named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T07:18:57Z)
• Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning [0.0]
  We propose Learn to SuperResolve Brain Graphs with Knowledge Distillation Network (L2S-KDnet) to superresolve brain graphs. Our teacher network is a graph encoder-decoder that firstly learns the LR brain graph embeddings, and secondly learns how to align the resulting latent representations to the HR ground truth data distribution. Next, our student network learns the knowledge of the aligned brain graphs as well as the topological structure of the predicted HR graphs transferred from the teacher.
  arXiv  Detail & Related papers  (2021-10-06T09:31:44Z)
• Increase and Conquer: Training Graph Neural Networks on Growing Graphs [116.03137405192356]
  We consider the problem of learning a graphon neural network (WNN) by training GNNs on graphs sampled Bernoulli from the graphon. Inspired by these results, we propose an algorithm to learn GNNs on large-scale graphs that, starting from a moderate number of nodes, successively increases the size of the graph during training.
  arXiv  Detail & Related papers  (2021-06-07T15:05:59Z)
• Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain Connectivity [0.0]
  We propose the first-ever deep graph super-resolution (GSR) framework that attempts to automatically generate high-resolution (HR) brain graphs. Our proposed AGSR-Net framework outperformed its variants for predicting high-resolution functional brain graphs from low-resolution ones.
  arXiv  Detail & Related papers  (2021-05-02T09:09:56Z)
• GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes [0.0]
  We introduce GSR-Net, the first super-resolution framework operating on graph-structured data that generates high-resolution brain graphs from low-resolution graphs. First, we adopt a U-Net like architecture based on graph convolution, pooling and unpooling operations specific to non-Euclidean data. Second, inspired by spectral theory, we break the symmetry of the U-Net architecture by topping it up with a graph super-resolution layer and two graph convolutional network layers to predict a HR graph.
  arXiv  Detail & Related papers  (2020-09-23T12:02:55Z)
• 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)
• Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis [49.05541693243502]
  We develop a framework for analyzing fMRI data using the Graph Isomorphism Network (GIN) One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space. We exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding.
  arXiv  Detail & Related papers  (2020-01-10T23:40:09Z)

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.