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General Departmental Seminar Series


Graph Identification and Alignment

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

Friday, November 9, 2007
12:00 pm
5725 MSC


Within the machine learning community, there has been a  
growing interest in learning structured models from input data that 
is itself structured.  Graph identification refers to methods that 
transform an observed input graph into an inferred output graph. 
Examples include inferring organizational hierarchies from social 
network data and identifying gene regulatory networks from protein-
protein interactions.  Graph alignment refers to a related problem, 
the problem of matching nodes, edges, and larger subgraphs between 
two graphs.  The key processes in graph identification and alignment 
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, and show how these methods are relevant to foundational 
problems in AI such as knowledge representation, reformulation, and 

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