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anchor-experiments

Experiments for AAAI anchor paper

autoiot

awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

botnet-detection-traffic

Topological botnet detection datasets and graph neural network applications

cf-gnnexplainer

deepexplain

A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

dig

A library for graph deep learning research

enrl

evoiot

fcil

This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.

gnn-model-explainer

gnn explainer

graph-network-explainability

Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)

graphmask

Official implementation of GraphMask as presented in our paper Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking.

ie-hgcn

Interpretable and Efficient Heterogeneous Graph Convolutional Network, IEEE TKDE 2021

kitnet-py

KitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.

lemna_dnn

Source code for 'Lemna: Explaining deep learning based security applications'.

lemna_rnn

lime

Lime: Explaining the predictions of any machine learning classifier

lime-experiments

Code for all experiments.

lrv

Layerwise Relevance Visualization in Convolutional Text Graph Classifiers

mate

MetA-Train to Explain

pgexplainer

Parameterized Explainer for Graph Neural Network

pgmexplainer

Generating PGM Explanation for GNN predictions

pics

pythonspidernotes

Python入门网络爬虫之精华版

re-parameterizedexplainerforgraphneuralnetworks

saliency_investigation

Code for "Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability" (https://arxiv.org/abs/2010.09750)

scikit-learn

scikit-learn: machine learning in Python

softawre-phylogenic-classification

IoT botnet malwares