Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Minimal and clean examples of machine learning algorithms implemented in Julia
Pytorch Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.
[CVPR 2024] Official code for "CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation"
A NetworkX implementation of Label Propagation from a "Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks" (Physical Review E 2008).
A tensorflow implementation of GCN-LPA
✨🧬 Refined binning of metagenomic contigs using assembly graphs
The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
Code for "Training-free Graph Neural Networks and the Power of Labels as Features" (TMLR 2024)
Semi-supervised learning via Compact Latent Space Clustering
Dynamic Graph-Based Label Propagation for Density Peaks Clustering
Multi Atlas Segmentation and Morphometric analysis toolkit (MASMAT) for mouse brain MRI
Code and models accompanying "Learning to Associate Words and Images Using a Large-scale Graph"
Computer Science & Engineering Master's Degree Graduation Thesis. Clustering Graphs - Applying a Label Propagation Algorithm to Detect Communities (in academia) in Graph Databases (ArangoDB). Communities were detected, a GraphQL API with NodeJS and Express and a frontend interface with React, TypeScript and CytoscapeJS were built.
Incremental Label Propagation (ILP) - Incremental Semi-Supervised Learning from Streams for Object Classification
Label propagation algorithm for community detection based on node importance and label influence
Code and Datasets for paper "Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports"
Mask Propagation and Active Learning in Medical CT Image Segmentation
This repository contains the code implementation used in the paper: "Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery"
A DGL implementation of "Combining Label Propagation and Simple Models Out-performs Graph Neural Networks" (ICLR 2021).
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