Computing That Serves

Graph Identification


Thursday, September 18, 2008 - 12:00pm


Lise Getoor
Associate Professor, Computer Science Department, University of Maryland, College Park

Within the machine learning and data mining communities, there has been a growing interest in learning structured models from input data that is itself structured. Graph identification refers to methods that transform observational data described as a noisy, input graph into an inferred "clean" output graph. Examples include inferring organizational hierarchies from social network data, identifying gene regulatory networks from protein-protein interactions, and understanding visual scenes based on inferred relationships among image parts. The key processes in graph identification are: entity resolution, link prediction, and collective classification. I will overview algorithms for these tasks, discuss the need for integrating the results to solve the overall problem collectively.


Lise Getoor is an associate professor in the Computer Science Department at the University of Maryland, College Park. She received her PhD from Stanford University in 2001. Her current work includes research on link mining, statistical relational learning and representing uncertainty in structured and semi-structured data. She has published numerous articles in machine learning, data mining, database, and artificial intelligence forums. She was awarded an NSF Career Award, is an action editor for the Machine Learning Journal, is a JAIR associate editor, has been a member of AAAI Executive council, and has served on a variety of program committees including AAAI, ICML, IJCAI, KDD, SIGMOD, UAI, VLDB, and WWW.