This project implements a semi-supervised approach to classify UN speeches. Utilized BERT, Gensim, Node2Vec and Tensorflow
-
Updated
Mar 29, 2024 - Python
This project implements a semi-supervised approach to classify UN speeches. Utilized BERT, Gensim, Node2Vec and Tensorflow
A curated list of community detection research papers with implementations.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Torch geometric compatible node embedders
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
Code for the Paper "Constructing Co-occurrence Network Embeddings to Assist Association Extraction for COVID-19 and Other Coronavirus Infectious Diseases"
A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)
The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".
A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).
A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).
A collection of important graph embedding, classification and representation learning papers with implementations.
A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
A scalable implementation of "Learning Structural Node Embeddings Via Diffusion Wavelets (KDD 2018)".
This repo contains scripts to crawl, analyze and model wikipedia user vandalism patterns.
Add a description, image, and links to the node2vec topic page so that developers can more easily learn about it.
To associate your repository with the node2vec topic, visit your repo's landing page and select "manage topics."