Building a Full Stack Social Network Web Application with React JS using Redux, NodeJS, Socket.IO and MongoDB. Utilizing Python's NetworkX library to represent graphs and the FastAPI framework to display follower suggestion results on the frontend. The Link Prediction Model includes algorithms such as CN, Jaccard, AA, and Katz

This repository uses semantic artist similarity to visualize artist networks based on Last.fm data. It utilizes Python with NetworkX for network analysis as well as Plotly and D3.js for interactive visualization.

Traveling Salesman Problem using linear programming math model, Reinforcement Learning and Simulated Annealing

An introduction to network analysis and applied graph theory using Python and NetworkX

Determining the Shortest Route, Transforming Graph to Adjacency Matrix and Incidence Matrix using Dijkstra's Algorithm in Python with NetworkX

A general purpose framework for building and running computational graphs.

This repository contains files related to my project on Unicorn Companies Data Analysis. The project utilizes R Studio, Python, Tableau, and Microsoft Excel. Feel free to explore the files and reports to gain insights into global unicorn companies.

Web application for a forum website with accessibility

Shipping service project for learning purposes

A program that suggests university courses based on your program and provides a custom GUI to customize your course plan. Created to help UofT students.

Datamining en Ciencia y Tecnología - Trabajos Prácticos 2 - Redes de palabras

Script to constructing a network from Wikipedia pages

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Visualization of skills to CS jobs with degree centrality and clustering coefficient

Using OSMnx, OSRM, and Google Maps Directions API with Python to calculate shortest, fastest, and traffic-based most-efficient routes for a set of origin and destination points

Legal Case Comparison System for Similar Situations 相似情境之法律案例對照系統

In this study, we propose a geolocation method based on named entities that are present in user tweets. We try to predict the US states where Twitter users reside in. We present a base method, compare it with a common approach in literature, and also examine some design decisions about our method.