Visiting Speaker
Stratis Ioannidis
Assistant Professor, Electrical and Computer Engineering Dept., Northeastern University
A Family of Tractable Graph Distances
Friday
Mar 29, 2019
Watch video
4:00 pm
177 Huntington Ave
11th floor

Important data mining problems such as nearest-neighbor search and clustering admit theoretical guarantees when restricted to objects embedded in a metric space. Graphs are ubiquitous, and clustering and classification over graphs arise in diverse areas, including, e.g., image processing and social networks. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics and thus come with no guarantees. Classic graph distances such as, e.g., the chemical and the Chartrand-Kubiki-Shultz (CKS) distance are arguably natural and intuitive, and are indeed also metrics, but they are intractable: as such, their computation does not scale to large graphs. We define a broad family of graph distances, that includes both the chemical and= the CKS distance, and prove that these are all metrics. Crucially, we show that our family includes metrics that are tractable. We demonstrate the scalability of our metrics by parallelizing their computation over Apache Spark: we can compute distances between graphs having 0.5M nodes and 3M edges over 400 CPUs within a few hours.

This is joint work with Jose Bento, Armin Moharrer, Shinkun Wang, and Jasmine Gao.

About the speaker
Stratis Ioannidis is an assistant professor in the Electrical and Computer Engineering Department of Northeastern University, in Boston, MA, where he also holds a courtesy appointment with the College of Computer and Information Science. He received his B.Sc. (2002) in Electrical and Computer Engineering from the National Technical University of Athens, Greece, and his M.Sc. (2004) and Ph.D. (2009) in Computer Science from the University of Toronto, Canada. Prior to joining Northeastern, he was a research scientist at the Technicolor research centers in Paris, France, and Palo Alto, CA, as well as at Yahoo Labs in Sunnyvale, CA. He is the recipient of an NSF CAREER Award, a Google Faculty Research Award, and a best paper award at ACM ICN 2017. His research interests span machine learning, distributed systems, networking, optimization, and privacy.
Visiting Speaker
Stratis Ioannidis
Assistant Professor, Electrical and Computer Engineering Dept., Northeastern University
A Family of Tractable Graph Distances
Fri
Mar 29, 2019
4:00 pm
177 Huntington Ave
11th floor
ADD to calendar

Important data mining problems such as nearest-neighbor search and clustering admit theoretical guarantees when restricted to objects embedded in a metric space. Graphs are ubiquitous, and clustering and classification over graphs arise in diverse areas, including, e.g., image processing and social networks. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics and thus come with no guarantees. Classic graph distances such as, e.g., the chemical and the Chartrand-Kubiki-Shultz (CKS) distance are arguably natural and intuitive, and are indeed also metrics, but they are intractable: as such, their computation does not scale to large graphs. We define a broad family of graph distances, that includes both the chemical and= the CKS distance, and prove that these are all metrics. Crucially, we show that our family includes metrics that are tractable. We demonstrate the scalability of our metrics by parallelizing their computation over Apache Spark: we can compute distances between graphs having 0.5M nodes and 3M edges over 400 CPUs within a few hours.

This is joint work with Jose Bento, Armin Moharrer, Shinkun Wang, and Jasmine Gao.

about the speaker
Stratis Ioannidis is an assistant professor in the Electrical and Computer Engineering Department of Northeastern University, in Boston, MA, where he also holds a courtesy appointment with the College of Computer and Information Science. He received his B.Sc. (2002) in Electrical and Computer Engineering from the National Technical University of Athens, Greece, and his M.Sc. (2004) and Ph.D. (2009) in Computer Science from the University of Toronto, Canada. Prior to joining Northeastern, he was a research scientist at the Technicolor research centers in Paris, France, and Palo Alto, CA, as well as at Yahoo Labs in Sunnyvale, CA. He is the recipient of an NSF CAREER Award, a Google Faculty Research Award, and a best paper award at ACM ICN 2017. His research interests span machine learning, distributed systems, networking, optimization, and privacy.