162 Open source projects that are alternatives of or similar to awesome-graph-explainability-papers

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Visualization tool for Graph Neural Networks

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Fit interpretable models. Explain blackbox machine learning.

Data generator for Arena - interactive XAI dashboard

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

This is a Pytorch implementation of GraphLIME

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

Fast approximate Shapley values in R

Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

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MindsDB server allows you to consume and expose MindsDB workflows, through http.

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

[ACL 2021] KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction

Variants of Vision Transformer and its downstream tasks

Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)

Locally Private Graph Neural Networks (ACM CCS 2021)

Message Passing Neural Networks for Simplicial and Cell Complexes

TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding (code & data)

A curated list of awesome graph & self-supervised-learning-based recommendation.

Geometric Deep Learning for Flux

Using deep learning to discover interpretable representations for mammogram classification and explanation

🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba 来自阿里巴巴的一站式大规模图计算系统 图分析 图查询 图机器学习

STAGIN: Spatio-Temporal Attention Graph Isomorphism Network

A collection of ML related stuff including notebooks, codes and a curated list of various useful resources such as books and softwares. Almost everything mentioned here is free (as speech not free food) or open-source.

Model verification, validation, and error analysis

No description or website provided.

Official code for "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization" (ICLR 2020, spotlight)

An alternative implementation of Recursive Feature and Role Extraction (KDD11 & KDD12)

Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.

Tutorials for Machine Learning on Graphs

A spherical CNN for weather forecasting

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

[ICLR 2021] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision

Reimplementation of Graph Embedding methods by Pytorch.

A repository for explaining feature attributions and feature interactions in deep neural networks.

Code for the paper 'An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem' (INFORMS Annual Meeting Session 2019)

Implementation of TheWebConf 2021 -- Few-shot Network Anomaly Detection via Cross-network Meta-learning

Implementation of the paper "Shapley Explanation Networks"

Overlapping community detection in Large-Scale Networks using BigCLAM model build on Apache Spark

A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

Official implementation of Neural Bellman-Ford Networks (NeurIPS 2021)

An index of recommendation algorithms that are based on Graph Neural Networks.

AAAI'21: Data Augmentation for Graph Neural Networks

Probabilistic Type Inference using Graph Neural Networks

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Awesome Graph Self-Supervised Learning

Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).