When: Friday, May 14, 2021
Speaker: Chris Connell
Title: The DynACPD network embedding algorithm for prediction tasks on dynamic networks
Abstract: Classical network embeddings create a low dimensional representation of the learned relationships between features across nodes. Such embeddings are important for tasks such as link prediction and node classification. We consider low dimensional embeddings of “dynamic networks” — a family of time varying networks where there exist both temporal and spatial link relationships between nodes. We present novel embedding methods for a dynamic network based on higher order tensor decompositions for tensorial representations of the dynamic network. Our embeddings are analogous to certain classical spectral embedding methods for static networks. We demonstrate the effectiveness of our approach by comparing our algorithms’ performance on the link prediction task against an array of current baseline methods across three distinct real-world dynamic networks. Finally, we provide a mathematical rationale for this effectiveness in the regime of small incremental changes. This is joint work with Yang Wang.
Chris Connell is interested in problems at the interface between dynamical systems, random walks, and the topology and geometry surrounding nonpositive curvature. Some of his recent work emphasizes bringing tools from these disciplines to bear on network embedding problems. He received his PhD from the University of Michigan and is now a professor of mathematics at Indiana University Bloomington.