Linkage-based multi-object clustering/grouping using GCN
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Updated
Aug 23, 2022 - Jupyter Notebook
Linkage-based multi-object clustering/grouping using GCN
PyTorch implementation of graph convolutional networks (GCNs).
Graph Convolutional Networks
STAD-GCN: Spatial-Temporal Attention-based Dynamic Graph Convolutional Network for Retail Market Price Prediction, pytorch version (ESWA 2024)
The implementation, training and evaluation of a Structure Seer machine learning model designed for reconstruction of adjacency of a molecular graph from the labelling of its nodes.
Implementation of various collaborative filtering methods for recommender systems with implicit feedback
Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary Metaheuristics
IE532: Analysis of Network Data in 2017 Fall, UIUC
Code for my Master's thesis "Exploiting Spatial-Temporal Relationships for Occlusion-Robust 3D Human Pose Estimation" at TUM
Supervised node classification using Graph Convolutional Network (GCN) in DGL.ai.
Calculating the nearest weather sensor for each traffic sensor and then merging the weather sensors' temporal data with the traffic sensors'.
A novel method for link prediction in temporal networks based on EvolveGCN (Aldo Pareja et al) and GAT (Petar Velickovic et al)
The repository is a collection of Jupyter notebooks showcasing various projects related to graph neural networks (GNNs). Each notebook provides a detailed explanation of the project and its implementation, making it easy for users to understand and replicate the results.
Graph Convolutional Branch and Bound solver for the Traveling Salesman Problem.
Predicting probable drug-binding sites for thousands of human proteins using AlphaFold2 predicted 3D protein structures.
Modeling the external convergence from photometric catalogs
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