Evolution of Graph Algorithms – Benefits and Challenges
A Talk by Ebru Cucen
Data Consultant | Mentor / Coach,
Graph Aware | Open Credo
About this talk
Graphs are the most native format to represent the data, it has the built-in structure to store the contextual information and enable data analysts to get better answers for their queries, data engineers to easily extend their ecosystem, and data scientists to build enhanced models.
With Graphs, we can make better-informed decisions. In this presentation, you will learn about the evolution of Graphs with a review on knowledge graphs, path-finding, clustering, and recommendation algorithms, how they are built, and their use cases.
Also, we will review the challenges we face as processing more data comes with a cost, and how they are solved in different faces of the machine learning lifecycle.
Join us for a 1-hour tour of Graph.
Historical timeline of Graph
The use cases for multiple industries
Knowledge graphs, path-finding algorithms, clustering, and recommendation algorithms
Graph Neural Networks
The challenges with GNNs, sampling, calculating loss functions, embeddings.
Machine Learning Engineers
Data Tech Leads
Beginner - Intermediate
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