Neo4j Live Demo: Graph Data Science and Machine Learning
Predictive Modeling and Analytics with Graph Data Science for Better Machine Learning
Thinking about incorporating relationships into your data to improve machine learning? Or maybe you’re creating a knowledge graph to investigate dependencies and find counterfactuals.
Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract network structure to use at scale in machine learning (ML). With graphs, relationships are embedded in the data itself, making it practical to add predictive capabilities to your existing practices.
Join our 30-minute demo using the Neo4j Graph Data Science Library, which has 60+ supported, scalable graph algorithms that range from pathfinding and similarity to influencer and community detection. This live demo provides essential resources for predictive modeling and analytics with graph technology.
Our experts will introduce and guide you through these learnings:
* Leveraging graph-native ML to make breakthrough predictions
* Taking different approaches to graph feature engineering, from queries and algorithms to embeddings
* Using graph-based ML techniques that draw from classical network science approaches, deep learning, and graph convolutional neural networks
You’ll leave this demo with resources for predictive modeling and analytics supplied by Neo4j:
* Algorithms such as pathfinding, centrality, and similarity
* Cutting-edge tech with graph embeddings and model training
* Supervised machine learning to predict unobserved or future relationships
* No-code visualization and prototyping with Bloom
David Allen is Technology Partner Architect at Neo4j. He is a deeply technical generalist with experience in managing teams and driving towards complex goals. The most fun he has had in his career is when he is learning something new, or trying to figure out how to do something that hasn’t been done before.