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pyg-team / pytorch_geometric

Star 14.3k
awesome-graph-classification

benedekrozemberczki / awesome-graph-classification

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Star 4.4k

A collection of important graph embedding, classification and representation learning papers with implementations.
deepwalk kernel-methods attention-mechanism network-embedding graph-kernel graph-kernels graph-convolutional-networks classification-algorithm node2vec weisfeiler-lehman graph-embedding graph-classification graph-attention-networks graph-representation-learning graph2vec deep-graph-kernels netlsd graph-attention-model structural-attention node-embedding
  • Updated Mar 2, 2022
  • Python

stellargraph / stellargraph

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Consolidate example graphs in unit tests

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adocherty
adocherty commented Nov 27, 2019

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

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good first issue sg-library
pytorch_geometric_temporal

benedekrozemberczki / pytorch_geometric_temporal

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Star 1.5k

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
deep-learning network-science pytorch temporal-networks spatial-analysis spatial-data spatiotemporal network-embedding spatio-temporal-analysis graph-convolutional-networks gcn spatio-temporal-data temporal-data graph-embedding graph-neural-networks node-embedding graph-convolution gnn temporal-graphs pypi-url pypi-image
  • Updated Apr 5, 2022
  • Python

shubhomoydas / ad_examples

Star 755

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
streaming timeseries time-series lstm generative-adversarial-network gan rnn autoencoder ensemble-learning trees active-learning concept-drift graph-convolutional-networks interpretability anomaly-detection adversarial-attacks explaination anogan unsuperivsed nettack
  • Updated Feb 10, 2022
  • Python

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