GraphConnect 2018 has ended
Thursday, September 20 • 2:15pm - 3:00pm
DeepWalk - Turning Graphs into Features via Network Embeddings

Sign up or log in to save this to your schedule, view media, leave feedback and see who's attending!

Feedback form is now closed.
Random walk algorithms help better model real-world scenarios and when applied to graphs can significantly improve machine learning. DeepWalk is a supervised learning algorithm developed to analyze graphs for classification, clustering, similarity search, and representations for statistical models. Developed by Dr. Skiena and his team, its wide applicability in data mining and information retrieval, DeepWalk has become extremely popular, having been cited by over 700 research papers since its publication at KDD 2014. In this talk, we’ll look how this graph algorithm exploits an appealing analogy between sentences as sequences of words and random walks as sequences of vertices to transfer deep learning (unsupervised feature learning) techniques from natural language processing to network analysis. Dr. Skiena will introduce the notion of graph embeddings, explain how DeepWalk constructs them, and demonstrate why they make such powerful features for machine learning applications.

In this session, you’ll learn about the motivations for graph enhanced machine learning and how vector representations are learned using walks (usually random walks) on the vertices in the graph. We’ll also look at the challenges of using other matrix factorization techniques like principal component analysis (PCA) and singular value decomposition (SVD). Mark Needham will review how random walk algorithms can be implemented within Neo4j.

avatar for Michael Hunger

Michael Hunger

Developer Relations, Neo4j
Michael Hunger has been passionate about software development for a very long time. For the last few years he has been working on the open source Neo4j graph database filling many roles.As caretaker of the Neo4j community and ecosystem he especially loves to work with graph-related projects, users... Read More →
avatar for Dr. Steven Skiena

Dr. Steven Skiena

Distinguished Teaching Professor of Computer Science, Stony Brook University
Steven Skiena is Distinguished Teaching Professor of Computer Science at Stony Brook University. His research interests include the design of graph, string, and geometric algorithms, and their applications (particularly to biology). He is the author of six books, including “The... Read More →

Thursday September 20, 2018 2:15pm - 3:00pm EDT
5. O'Neil
  Neo4j Session, AI and Machine Learning