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Top 23 Python graphneuralnetwork Projects

Project mention: PyTorch Geometric – Graph Neural Network Library for PyTorch  news.ycombinator.com  20240624

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Project mention: anomalydetectionresources: NEW Extended Research  star count:7507.0  /r/algoprojects  20231024

Project mention: RecBole – A unified, comprehensive and efficient recommendation library  news.ycombinator.com  20240117

SuperGluePretrainedNetwork
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)


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Project mention: RAG Using Structured Data: Overview and Important Questions  news.ycombinator.com  20240110
Ok, using ChatGPT and Bard (the irony lol) I learned a bit more about GNNs:
GNNs are probabilistic and can be trained to learn representations in graphstructured data and handling complex relationships, while classical graph algorithms are specialized for specific graph analysis tasks and operate based on predefined rules/steps.
* Why is PyG it called "Geometric" and not "Topologic" ?
Properties like connectivity, neighborhoods, and even geodesic distances can all be considered topological features of a graph. These features remain unchanged under continuous deformations like stretching or bending, which is the defining characteristic of topological equivalence. In this sense, "PyTorch Topologic" might be a more accurate reflection of the library's focus on analyzing the intrinsic structure and connections within graphs.
However, the term "geometric" still has some merit in the context of PyG. While most GNN operations rely on topological principles, some do incorporate notions of Euclidean geometry, such as:
 Node embeddings: Many GNNs learn lowdimensional vectors for each node, which can be interpreted as points in a vector space, allowing geometric operations like distances and angles to be applied.
 Spectral GNNs: These models leverage the eigenvalues and eigenvectors of the graph Laplacian, which encodes information about the geometric structure and distances between nodes.
 Manifold learning: Certain types of graphs can be seen as lowdimensional representations of highdimensional manifolds. Applying GNNs in this context involves learning geometric properties on the manifold itself.
Therefore, although topology plays a primary role in understanding and analyzing graphs, geometry can still be relevant in certain contexts and GNN operations.
* Real world applications:
 HuggingFace has a few models [0] around things like computational chemistry [1] or weather forecasting.
 PyGod [2] can be used for Outlier Detection (Anomaly Detection).
 Apparently ULTRA [3] can "infer" (in the knowledge graph sense), that Michael Jackson released some disco music :p (see the paper).
 RGCN [4] can be used for knowledge graph link prediction (recovery of missing facts, i.e. subjectpredicateobject triples) and entity classification (recovery of missing entity attributes).
 GreatX [5] tackles removing inherent noise, "Distribution Shift" and "Adversarial Attacks" (ex: noise purposely introduced to hide a node presence) from networks. Apparently this is a thing and the field is called "Graph Reliability" or "Reliable Deep Graph Learning". The author even has a bunch of "awesome" style lists of links! [6]
 Finally this repo has a nice explanation of how/why to run machine learning algorithms "outside of the DB":
"Pytorch Geometric (PyG) has a whole arsenal of neural network layers and techniques to approach machine learning on graphs (aka graph representation learning, graph machine learning, deep graph learning) and has been used in this repo [7] to learn link patterns, also known as link or edge predictions."

0: https://huggingface.co/models?pipeline_tag=graphml&sort=tre...
1: https://github.com/Microsoft/Graphormer
2: https://github.com/pygodteam/pygod
3: https://github.com/DeepGraphLearning/ULTRA
4: https://huggingface.co/rishipnv/RGCN
5: https://github.com/EdisonLeeeee/GreatX
6: https://edisonleeeee.github.io/projects.html
7: https://github.com/Orbifold/pyglinkprediction

deep_gcns_torch
Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org


graphtransformer
Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLGAAAI'21.




EquiBind
EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein


pna
Implementation of Principal Neighbourhood Aggregation for Graph Neural Networks in PyTorch, DGL and PyTorch Geometric

STEP
Code for our SIGKDD'22 paper PretrainingEnhanced SpatialTemporal Graph Neural Network For Multivariate Time Series Forecasting. (by GestaltCogTeam)




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Python graphneuralnetworks discussion
Python graphneuralnetworks related posts

PyTorch Geometric – Graph Neural Network Library for PyTorch

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MetaPath2Vec from Pytorch geometric with HeteroData Dataset

[N] PyG 2.3.0 released: PyTorch 2.0 support, native sparse tensor support, explainability and accelerations

Ask HN: ML Papers to Implement

A note from our sponsor  InfluxDB
www.influxdata.com  16 Sep 2024
Index
What are some of the best opensource graphneuralnetwork projects in Python? This list will help you:
Project  Stars  

1  pytorch_geometric  20,936 
2  dgl  13,355 
3  anomalydetectionresources  8,228 
4  RecBole  3,330 
5  SuperGluePretrainedNetwork  3,174 
6  pytorch_geometric_temporal  2,615 
7  spektral  2,365 
8  ogb  1,916 
9  torchdrug  1,424 
10  pygod  1,298 
11  deep_gcns_torch  1,133 
12  DeepRobust  972 
13  graphtransformer  848 
14  DGFraud  677 
15  qagnn  620 
16  efficientgnns  526 
17  EquiBind  464 
18  dance  346 
19  pna  334 
20  STEP  325 
21  DiffSBDD  316 
22  how_attentive_are_gats  293 
23  PyNeuraLogic  277 