Gnn Model Explainer

تعمل هذه المجموعة كإطار عمل لتفسير عمليات اتخاذ القرار في الشبكات العصبية الرسومية (graph neural networks). تعمل كمكتبة لتحليل كيفية معالجة هذه النماذج لبيانات الشبكة المعقدة، وتوفر طرقاً لتحديد سمات العقد المحددة والأنماط الهيكلية التي تؤثر على النتائج التنبؤية.

يتميز المشروع باستخدام استخراج المخططات الفرعية المحسّن بالقناع (mask-optimized subgraph extraction) وتعيين الإسناد القائم على التدرج لعزل المكونات الدنيا للرسم البياني التي تحافظ على التنبؤ الأصلي للنموذج. من خلال فصل طبقات معالجة الرسم البياني عن منطق التفسير، تسمح البنية بتحليل متسق عبر أنواع مختلفة من الشبكات العصبية الرسومية، بما في ذلك النماذج التلافيفية والقائمة على الانتباه.

بعيداً عن التفسير الأساسي، تدعم المكتبة تدريب نماذج الرسوم البيانية على مجموعات بيانات اصطناعية وواقعية وتسهل تصحيح أخطاء نماذج تعلم الآلة. تتضمن إمكانيات لتصور تفسيرات النموذج من خلال دفاتر ملاحظات تفاعلية، مما يتيح عرض خرائط حرارة الإسناد وهياكل الشبكة المعقدة للمساعدة في تحديد الميزات والمخططات الفرعية المؤثرة.

Features

Graph Neural Network Explainers - Identifies influential subgraphs and node features that drive predictive outcomes in graph neural network models.
Model Interpretability Tools - Determine which specific node attributes or data points have the greatest impact on the decision-making process of a model when making predictions based on graph data.
Graph - Analyzes how graph-based machine learning models make predictions by identifying influential nodes and structural patterns.
Mask-Optimized - Learns binary masks over graph components to isolate the minimal structural subset that preserves the original model prediction.
Feature Importance Attribution - Quantifies the relative contribution of individual input variables to the predictions of machine learning models.
Attribution Gradients - Computes feature importance by backpropagating model outputs through the computational graph to input gradients.
Graph Data Analysis Libraries - Provides a collection of routines for training graph convolutional and attention models on network datasets.
Graph Message Passing Frameworks - Implements neural message passing primitives to learn representations of nodes and edges in graph structures.
Graph Neural Network Training Routines - Provides routines for training graph convolutional and attention models on synthetic and real-world datasets.
Machine Learning Debugging - Investigates the internal logic of complex neural networks to identify biases or errors in graph data processing.
Model Explanation Visualizations - Renders generated masks and model insights through interactive notebooks to facilitate detailed analysis of model interpretations.
Network Graph Visualization - Renders complex network structures and model insights into clear visual formats to help researchers understand data flow.
Subgraph Matching - Isolates specific structural components within a graph that contribute most significantly to a model prediction.

سجل النجوم

RexYinggnn-model-explainer

Name: rexying/gnn-model-explainer
Author: RexYing

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1,051 نجوم192 تفرعاتPythonApache-2.04 مشاهدات

Gnn Model Explainer

Features

Graph Neural Network Explainers - Identifies influential subgraphs and node features that drive predictive outcomes in graph neural network models.
Model Interpretability Tools - Determine which specific node attributes or data points have the greatest impact on the decision-making process of a model when making predictions based on graph data.
Graph - Analyzes how graph-based machine learning models make predictions by identifying influential nodes and structural patterns.
Mask-Optimized - Learns binary masks over graph components to isolate the minimal structural subset that preserves the original model prediction.
Feature Importance Attribution - Quantifies the relative contribution of individual input variables to the predictions of machine learning models.
Attribution Gradients - Computes feature importance by backpropagating model outputs through the computational graph to input gradients.
Graph Data Analysis Libraries - Provides a collection of routines for training graph convolutional and attention models on network datasets.
Graph Message Passing Frameworks - Implements neural message passing primitives to learn representations of nodes and edges in graph structures.
Graph Neural Network Training Routines - Provides routines for training graph convolutional and attention models on synthetic and real-world datasets.
Machine Learning Debugging - Investigates the internal logic of complex neural networks to identify biases or errors in graph data processing.
Model Explanation Visualizations - Renders generated masks and model insights through interactive notebooks to facilitate detailed analysis of model interpretations.
Network Graph Visualization - Renders complex network structures and model insights into clear visual formats to help researchers understand data flow.
Subgraph Matching - Isolates specific structural components within a graph that contribute most significantly to a model prediction.

بدائل مفتوحة المصدر لـ Gnn Model Explainer

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عرض جميع البدائل الـ 30 لـ Gnn Model Explainer

مجموعات مختارة تضم Gnn Model Explainer

مجموعات منسقة بعناية يظهر فيها Gnn Model Explainer.

أدوات تفسير نماذج تعلم الآلة

الأسئلة الشائعة

ما هي وظيفة rexying/gnn-model-explainer؟

ما هي الميزات الرئيسية لـ rexying/gnn-model-explainer؟

الميزات الرئيسية لـ rexying/gnn-model-explainer هي: Graph Neural Network Explainers, Model Interpretability Tools, Graph, Mask-Optimized, Feature Importance Attribution, Attribution Gradients, Graph Data Analysis Libraries, Graph Message Passing Frameworks.

ما هي البدائل مفتوحة المصدر لـ rexying/gnn-model-explainer؟

تشمل البدائل مفتوحة المصدر لـ rexying/gnn-model-explainer: christophm/interpretable-ml-book — This project is a comprehensive educational resource and technical manual focused on interpretable machine learning… interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training… pair-code/lit — Lit is a machine learning interpretability framework and model debugging tool designed to analyze model behavior and… cdpierse/transformers-interpret — Transformers-interpret is a diagnostic library designed for the interpretability of transformer-based machine learning… catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… slundberg/shap — SHAP is a machine learning explainer that uses a game-theoretic framework to estimate the contribution of each feature…

Gnn Model Explainer

Features

سجل النجوم

Gnn Model Explainer

Features

بدائل مفتوحة المصدر لـ Gnn Model Explainer

christophM/interpretable-ml-book

interpretml/interpret

PAIR-code/lit

cdpierse/transformers-interpret

مجموعات مختارة تضم Gnn Model Explainer

الأسئلة الشائعة

سجل النجوم

مجموعات مختارة تضم Gnn Model Explainer

بدائل مفتوحة المصدر لـ Gnn Model Explainer

christophM/interpretable-ml-book

interpretml/interpret

PAIR-code/lit

cdpierse/transformers-interpret

الأسئلة الشائعة