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59 results for “graph neural networks”
Lifelong Learning of Graph Neural Networks for Open-World Node Classification
<p>Three temporal graph datasets for node classification under distribution shift.</p> <p>DBLP-Easy and DBLP-Hard are citation graph datasets. PharmaBio is a collaboration graph dataset.</p> <p>Vertices are scientific publications, edges are either citations (DBLP) or at-least-one-common-author relationships (PharmaBio).</p> <p>The task is to classify the vertices of the graph into the respective conference/journal venues (DBLP) or journal categories (PharmaBio). In the DBLP datasets, new classes may appear over time.</p> <p>Each dataset follows the structure:</p> <p>- adjlist.txt -- the graph structure encoded as adjacency lists: in each row, the first entry is the source vertex, the remaining entries are adjacent vertices</p> <p>- X.npy -- numpy serialized format for node features indexed by node id corresponding to adjlist.txt</p> <p>- y.npy -- numpy serialized format for node labels indexed by node id corresponding to adjlist.txt</p> <p>- t.npy -- numpy serialized format for time steps indexed by node id corresponding to adjlist.txt</p> <p>A paper describing our incremental training and evaluation framework is published in IJCNN 2021 (Pre-print on arXiv: <a href="https://arxiv.org/abs/2006.14422">https://arxiv.org/abs/2006.14422</a>).</p> <p>If you use these datasets in your research, please cite the corresponding paper:</p> <pre><code>@inproceedings{galke2021lifelong, author={Galke, Lukas and Franke, Benedikt and Zielke, Tobias and Scherp, Ansgar}, booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, title={Lifelong Learning of Graph Neural Networks for Open-World Node Classification}, year={2021}, volume={}, number={}, pages={1-8}, doi={10.1109/IJCNN52387.2021.9533412} }</code></pre> <p><br> </p>
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks
<p>String-like motions (SLMs) cooperative, "snake"-like movements of particles—are crucial for dynamics in diverse glass formers. Despite their ubiquity, questions persist: do SLMs prefer specific paths? If so, can we predict these paths? Here, in Al-Sm glasses, our iso-configurational ensemble simulations reveal that SLMs indeed follow certain paths. By designing a graph neural network (GNN) to featurize the environment around directional paths, we achieve a high-fidelity prediction of likely SLM pathways solely based on the static structure. GNN gauges a structural measure to assess each path's propensity to engage in SLMs, akin to a "softness" metric, but for paths rather than for atoms. Our GNN interpretation reveals the critical role of the bottleneck zone along paths in steering SLMs. By monitoring "path-softness", we elucidate SLM-favored paths transit from fragmented to interconnected upon glass transition. Our findings reveal that, beyond atoms or clusters, glasses have another dimension of structural heterogeneity: "paths".</p>
Protein vibrational frequencies dataset: Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks
<p>Dataset for machine learning model, based on graph neural network, to predict protein natural frequencies using Graph Neural Networks. </p> <p><strong>Code</strong>: https://github.com/lamm-mit/ProteinMechanicsGNN</p> <p><strong>Paper</strong>: </p> <p>Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks</p> <p>Kai Guo and Markus J. Buehler</p> <p><em>Digital Discovery</em>, 2022, DOI: 10.1039/D1DD00007A</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</p>
Graph Neural Network for Metal Organic Framework Potential Energy Approximation
<p>Data set consists of 50,000 different configurations for the Metal Organic Framework (MOF) FIGXAU. Was generated by randomly modifying the positions of the atoms and doing an SCF relaxation on each configuration.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Helheim Glacier, Greenland
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Helheim Glacier, Greenland.</p> <ul> <li>ISSM_DGL_Helheim.py: Python file for training GNN models</li> <li>ISSM_CNN_Helheim.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results</li> </ul>
The dataset used in the article "A point cloud graph neural network for protein-ligand binding site prediction"
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Raw datasets for paper "Multi-scale hydraulic graph neural networks for flood modelling"
<p>The repository contains two zip folders for the synthetic and case study datasets (raw_datasets_mesh.zip, raw_datasets_dk15.zip). </p> <p>Each zip folder comprises 4 subfolders (DEM, Geometry, Hydrograph, Simulations), containing the elevation, boundary polygon, discharge hydrograph, and full hydrodynamic results for all simulations.</p> <p>The overview.csv file provides the seeds used for experiment replicability and the runtime of the numerical model on each simulation.</p>
Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue
<p>Database of the article "Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue ".</p>
Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks
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Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs
<p><strong>Background: To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. Machine learning (ML) methods in general, and Deep Learning (DL) methods in particular, are a potential way forward because they are able to cope with the nonlinearity of natural systems. However, there are several barriers that exist, including the absence of ML-ready data. We propose to develop a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. A critical part of this framework are data transformation methods that map the heterogeneous input data into formats that are consumable by the ML techniques. The central hypothesis of this research is that deep learning algorithms and biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. Our long term goal is to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches. This pilot project on predicting emergent properties of complex systems and multidimensional interactions is funded by the NSF (Award # 1939945, 1940059, 1940062, 1940330). </strong></p> <p> </p> <p><strong>Results: We have established shared project governance, communication channels, project timeline, and data and computing environment across four universities. We have successfully reached out to three other projects for broader collaboration.</strong></p>
Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
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Datasets for "Physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data"
<div> <p>Datasets used for implementing the <a href="https://github.com/huankoh/PSICHIC">PSICHIC</a> experiments shown in the <a href="https://doi.org/10.1101/2023.09.17.558145">manuscript</a>.</p> <p> </p> </div>
Quantifying the spatial homogeneity of urban road networks via graph neural networks
<p>Publication: Quantifying the spatial homogeneity of urban road networks via graph neural networks, Nature Machine Intelligence, 2022.</p> <p>Publication DOI: 10.1038/s42256-022-00462-y</p> <p>Please refer to https://github.com/jiang719/road-network-predictability.</p>
Synthetic dataset and prediction files for the paper "Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction", by Costantino et al. (2024)
<p>Synthetic database used for training and evaluation of SSEdenoiser</p>
Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction
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A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms
<p>The dataset for "A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms" can be divided into training and validation sets at any desired ratio.</p> <p> </p> <p>The code has been published on GitHub: <a href="https://github.com/cqtian-kk/Lightning_Detection_Location">Lightning_Detection_Location</a> or <a href="https://zenodo.org/records/14048427">DOI 10.5281/zenodo.13350849</a></p>
Graph neural network emulator for modeling of ice dynamics and calving in the Pine Island Glacier, Antarctica
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Pine Island Glacier, Antarctica</p> <ul> <li>ISSM_DGL_PIG2.py: Python file for training GNN models (*single.py: code for single GPU environment)</li> <li>ISSM_CNN_PIG.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results (graphs for GNNs)</li> <li>*.pkl: Datasets of the ISSM transient simulation results (grids for CNNs)</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.