Representation learning on large graphs using stochastic graph convolutions.
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Updated
May 8, 2018 - Python
Representation learning on large graphs using stochastic graph convolutions.
GraphSAGE and GAT for link prediction.
PyTorch implementation of GraphSAGE.
This repo contains the experiments performed for link prediction, multi-class classification and pairwise node classification task.
1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.
Explained Graph Embedding generation and link prediction
Multi-label propagation on graphs with GraphSage
Senior Capstone Project: Graph-Based Product Recommendation
The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""
[ASAP 2020; FPGA 2020] Hardware architecture to accelerate GNNs (common IP modules for minibatch training and full batch inference)
Link prediction on the DBpedia graph of musical artists using GraphSAGE
CS224W Winter 2021 Machine learning with Graphs
Research Project I completed under Dr Vinti Agrawal at BITS Pilani.
Graph Clustering using different techniques. [Node2vec, GraphSAGE, Agglomerative]
Bachelor Thesis
Code implementation & CLI tool for the paper: "Graph Based Temporal Aggregation for Video Retrieval"
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