Topological Graph Neural Networks (ICLR 2022). Contribute to BorgwardtLab/TOGL development by creating an account on GitHub.
Graph neural networks (GNNs) have many applications, including the medical field (e.g., neuroscience), thanks to their flexibility and expressiveness in handling unstructured graph-based data. However, one major issue in applying GNNs for medical applications is their unscalability in handling large...
topological-data-analysisgraph-neural-networkshigher-order-modelshigher-order-networkshypergraph-neural-networkscw-complexhypergraph-learningcell-complex-networkssimplicial-neural-networkscell-complex-neural-networkscw-networkscxntopological-deep-learningcell-neural-networkssimplicial-message-passingcellular-message-passin...
In this paper, we focus on the utilisation of reactive trajectory imitation controllers for goal-directed visual navigation in mobile robotics. We propose topological navigation graph (TNG) framework. TNG is an imitation-learning-based topological navigation framework for navigating through environments wit...
The network created at each step is called a simplicial complex, a generalized type of graph that is discussed further in Section 3.2.2. Unlike standard networks, simplicial complexes can contain higher-dimensional analogs of edges between more than two points, allowing them to represent higher-dim...
Current complex prediction models are the result of fitting deep neural networks, graph convolutional networks or transducers to a set of training data. A key challenge with these models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. ...
Topological data analysis in medical imaging: current state of the art 2023, Insights into Imaging How to handle big data for disease stratification in respiratory medicine? 2023, Thorax Graph neural networks for image-guided disease diagnosis: A review 2023, iRADIOLOGY8 Lead contact ...
The topological attributes of structural covariance networks (SCNs) based on fractal dimension (FD) and changes in brain network connectivity were investigated using graph theory and network-based statistics (NBS) in patients with noise-induced hearing l
torchdiffeq: https://github.com/rtqichen/torchdiffeq. Training Graph Classification Comparison with RePHINE cd RePHINE/ python -u main_2d.py --dataset {PROTEINS_full/NCI109/NCI1/IMDB-BINARY} --gnn {gin/gcn} --diagram_type {standard/rephine} --nsteps 20 Comparison with TOGL cd RePHINE/...
This is a Tensorflow implementation of paper: Learning to Drop: Robust Graph Neural Network via Topological Denoising https://arxiv.org/abs/2011.07057 WSDM'21 Unofficial Implementation Robust Graph Representation Learning via Neural Sparsification ICML 20 Since the previous version is not easy to use,...