Research Project I completed under Dr Vinti Agrawal at BITS Pilani.

This repository provides an implementation of the DTi2Vec tool, to identify Drug-Target interaction using network embedding and ensemble learning

GraphML-based CAN Bus Attack Detection: Detects masquerade attacks in CAN using machine learning.

Code for embedding human phenotype ontology (HPO) using Node2Vec and incorporating phenotype frequencies from a hospital-size dataset. Showcased in the linked paper.

3rd place solution of FinNet Challenge Hackathon

Simple yet efficient algorithms for Link Prediction in Dynamic Graphs

Code for "On the Surprising Behaviour of node2vec"

Graph Clustering using different techniques. [Node2vec, GraphSAGE, Agglomerative]

This project implements a semi-supervised approach to classify UN speeches. Utilized BERT, Gensim, Node2Vec and Tensorflow

This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn

This notebook is part of a project on Graph Embedding Techniques. It's a comparison between DeepWalk and Node2Vec using SkipGram applied to a dataset called Github Social Network. I did several tests with different p and q values for Node2Vec, the last p = 0.25 and q = 0.25 were the ones that gave me the best result.

The aim is to Analyse and predict the possible interactions between the various subreddits present and predict the type of interaction and detect the implicit communities between subreddits This will be through various Network analysis and statistical inferences and predictive model

The aim is to Analyse and predict the possible interactions between the various subreddits present and predict the type of interaction and detect the implicit communities between subreddits This will be through various Network analysis and statistical inferences and predictive model

Machine learning approach to classify graph nodes based on topology