Introduction to Link Prediction and Different Models Used for Embeddings (1st Offering)
Presenter: Amangel Bhullar
Date: Thursday, March 20, 2025
Time: 10:00 am
Location: Workshop Space, 4th Floor - 300 Ouellette Ave., School of Computer Science Advanced Computing Hub
Link prediction is a fundamental task in network analysis, aiming to predict the likelihood of future or missing relationships between nodes in a graph. It is widely applied in various domains, including social networks, recommendation systems, and biological networks. The task involves using the structure of the graph and attributes of nodes and edges to infer potential links that may emerge or have been overlooked. Recent advances in research have led to the development of embedding-based models for link prediction. These models aim to represent nodes and edges as low-dimensional vectors, capturing the structural and relational properties of the graph.
- What is Link Prediction?
- Techniques for Link Prediction.
- Tensor Decomposition Models
- Geometric Models
- Deep learning models.
- Open Problems and Research Directions.
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Amangel Bhullar is a Ph.D. candidate in Computer Science at the University of Windsor, specializing in artificial intelligence with a focus on knowledge representation, machine learning, and knowledge graphs. She currently serves as the President of the Graduate Student Society (GSS), where she leads initiatives to enhance the academic and social experience of graduate students.
In addition to her role at GSS, Amangel is the Director of the Lancer Sport and Recreation Center (LSRC) Corporation and serves as a Member of the Board of Governors at the University of Windsor. Her leadership contributions extend further as an Ex-Officio Member of the University Senate, where she brings a student-centred perspective to university policy and governance matters.