Technical Workshop "GRAPH NEURAL NETWORKS (GNNS) FOR LINK PREDICTION (Unveiling the Power of Graphs in Predicting Connections)" By: Nahid Abdolrahmanpour

The School of Computer Science Presents..

GRAPH NEURAL NETWORKS (GNNS) FOR LINK PREDICTION
(Unveiling the Power of Graphs in Predicting Connections)

Presenter: Nahid Abdolrahmanpour

Date: Tuesday, January 23rd, 2024
Time: 4:00 pm – 5:00 pm
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub) 

Abstract:
Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing and predicting connections in complex systems represented as graphs. This workshop will provide a comprehensive introduction to GNNs and focus on their application in link prediction. The workshop aims to empower attendees with the knowledge and skills needed to leverage the potential of GNNs in uncovering hidden relationships within graph-structured data.

Workshop Outline:

Introduction to Graphs and Link Prediction

Fundamentals of Graph Neural Networks (GNNs)
Data Preparation for Link Prediction
Building a Graph Neural Network Model
Link Prediction with GNNs
Q&A and Discussion

Prerequisites:

Basic understanding of machine learning concepts
Knowledge of basic graph theory (nodes, edges, connectivity)
Prior exposure to neural networks is beneficial but not mandatory

Biography:
Nahid Abdolrahmanpour (Ph.D. Student in Computer Science) at the University of Windsor, With a solid foundation in Data Mining and Artificial Intelligence.
Research Focus: Social Network Analysis
Nahid has already earned a master's degree in computer science, specializing in Data Mining.