Event Detail

Event Type: 
Applied Mathematics and Computation Seminar
Thursday, April 25, 2019 - 16:00 to 17:00

Speaker Info

Purdue University

This event is in SIAM PNW distinguished speaker seminar online series. See below on details how to attend.

Spectral clustering is a well-known way to partition a graph or network into clusters or communities with provable guarantees on the quality of the clusters. This guarantee is known as the Cheeger inequality and it holds for undirected graphs. We'll discuss a new generalization of the Cheeger inequality to higher-order structures in networks including network motifs. This is easy to implement and seamlessly generalizes spectral clustering to directed, signed, and many other types of complex networks. In particular, our generalization allow us to re-use the large history of existing ideas in spectral clustering including local methods, overlapping methods, and relationships with kernel k-means. We will illustrate the types of clusters or communities found by our new method in biological, neuroscience, ecological, transportation, and social networks.

BIO: David Gleich is the Jyoti and Aditya Mathur Associate Professor in the Computer Science Department at Purdue University whose research is on novel models and fast large-scale algorithms for data-driven scientific computing including scientific data analysis, bioinformatics, and network analysis. He is committeed to making software available based on this research and has written software packages such as MatlabBGL with thousands of users worldwide. Gleich has received a number of awards for his research including a SIAM Outstanding Publication prize (2018), a Sloan Research Fellowship (2016), an NSF CAREER Award (2011), the John von Neumann post-doctoral fellowship at Sandia National Laboratories in Livermore CA (2009). His research is funded by the NSF, DOE, DARPA, and NASA.
For more information, see his website: https://www.cs.purdue.edu/homes/dgleich/

Please join the meeting from your computer, tablet or smartphone.
You can also dial in using your phone.
United States: +1 (669) 224-3412
Access Code: 501950733