Graph Embedding Techniques - Matrix Factorization to Deep Learning
May 11 | KGC 2023
•
26m
Graph embeddings can be used for a variety of applications, including recommendation, fraud detection, and other machine learning tasks. In this work, we aim to walk through various different embedding techniques, starting with spectral approaches, moving towards graph neural networks, and finally newer, inductive techniques such as NodePiece. Throughout the tutorial, we will be implementing algorithms using the (open-source) TigerGraph Machine Learning Workbench, a tool for easily training machine learning algorithms on large-scale graph datasets. Using a variety of datasets, we will discuss the pros and cons of each technique, demonstrate them working, and examine future directions for the field of graph embedding research from the lens of industry.
Up Next in May 11 | KGC 2023
-
When Not to Use a Graph db for Your K...
When people think of knowledge graphs and graph analytics they reflexively think they need a specialized database. That answer is often incomplete or just wrong for their workload.
We’ll analyze the 11 access patterns involved in graph analytics and build a logical and physical reference architec... -
The Missing Link in Knowledge Graphs
Graph-based technologies are appealing because they promise more flexibility than other database technologies. However, it turns out that graph-based databases still require data to be structured and are not that different from traditional databases, therefore creating disappointment for end user...
-
Structured Regulatory Compliance Thro...
Regulatory complexity is causing a heavy burden on financial institutions, especially when markets expect more rapid innovation to serve its needs. Meanwhile, regulators keep placing more and more expectations to protect the financial, economic and social systems. Despite AI’s great strides in te...
