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pathpyG
pathpy/pathpyG
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Getting Started
Getting Started
Installation
Installation
Docker Installation
Tutorials
Tutorials
Basic Concepts
Networks, Adjacency and Laplacian Matrices
Temporal Graphs
Interactive Graph Visualisation
Temporal Graph Visualisation using Manim
Generative Models for Random Graphs
Graph Learning in Netzschleuder Data
Advanced Concepts
Advanced Concepts
Graph Learning Foundations
Graph Learning Foundations
Laplacian Eigenmaps and Logistic Regression
Perceptron Classification
PyTorch, Autograd, and Stochastic Gradient Descent
Feed-Forward Neural Networks
Neural Message Passing in PyTorch Geometric
Graph Convolutional Networks
Node Classification with Additional Node Features
Semi-Supervised Learning with GCNs
Causality-Aware Temporal Network Analysis
Causality-Aware Temporal Network Analysis
Time-Respecting Paths, Centralities and Communities
Path Data and Higher-Order Models
Working with Higher-Order De Bruijn Graphs
Event Graphs
Higher-Order Models for Time-Respecting Paths
Causality-Aware Temporal Graph Learning
Causality-Aware Temporal Graph Learning
De Bruijn Graph Neural Networks
Why Standard Temporal GNNs Ignore Causality
Extending PathpyG
Extending PathpyG
Implementation Concepts
Develop your own plot Functions
Code Reference
Code Reference
pathpyG
pathpyG
algorithms
algorithms
centrality
components
generative_models
lift_order
rolling_time_window
shortest_paths
temporal
weisfeiler_leman
core
core
event_graph
graph
higher_order_graph
index_map
multi_order_model
path_data
temporal_graph
io
io
datasets
netzschleuder
pandas
nn
nn
dbgnn
statistics
statistics
clustering
degrees
node_similarities
utils
utils
config
config
Table of contents
config
convert
logger
progress
visualisations
visualisations
d3js
d3js
backend
manim
manim
backend
temporal_graph_scene
matplotlib
matplotlib
backend
tikz
tikz
backend
layout
network_plot
pathpy_plot
plot_backend
plot_function
temporal_network_plot
unfolded_network_plot
utils
Contributing
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