Master LLMs with our FREE course in collaboration with Activeloop & Intel Disruptor Initiative. Join now!


How To Create Powerful Embeddings From Topology Information In Graphs
Data Science   Latest   Machine Learning

How To Create Powerful Embeddings From Topology Information In Graphs

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

Convert your graph to a clustering-friendly format with this article. “give me an image of a graph where the edges are turning into the nodes” prompt. ChatGPT, OpenAI, 30 Jan. 2024. /g/g-2fkFE8rbu-dall-e.

· Motivation· Installing the required packages:· Assumptions· Deepwalk/Node2vec· GNNs· LINE· Apply clustering to the embeddings· Conclusion· References

Using a graph can be a good way of encoding lots of information. The graph, as a structure, should particularly be used when you have relationships within the data, which the graph can represent with edges. The edges are the essential component of the graph that makes it different from storing data in a tabular format. The graph structure can cause issues when running cluster methods, as clustering methods are not typically made to support taking in node information and edge information simultaneously. This tutorial will talk about how you can combine the node and edge information to run traditional node attribute-based clustering methods like KMeans or DBScan. Node attribute-based clustering refers to methods that solely take in a node embedding (for example, an (x,y) coordinate pair, which can be used to represent a node in 2D space).

To follow this tutorial, use the commands below to install the required packages:

pip install networkx==2.8.8pip install… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI

Feedback ↓