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PhD Defense - Salvador Aguinaga

Start: 11/15/2017 at 1:00PM
End: 11/15/2017 at 3:30PM
Location: 315 Stinson
Attendees: Faculty and students are welcome to attend the presentation portion of the defense. Light refreshments will be served.
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Salvador Aguinaga
Doctoral Defense
November 15, 2017        1:00 pm       315 Stinson Remick
Adviser:  Dr. Tim Weninger
Committee Members:
Dr. Nitesh Chawla        Dr. David Chiang        Dr. James Allen Evans


Generating Networks by Learning Hyperedge Replacement Grammars


 Network modeling is critical and central to the study of complex systems. Modeling enables researchers to examine emergent behavior and related phenomena, a mechanism we still know little about and one arising from the milieu of interacting patterns at the local level. These complex systems include the global economy, financial markets, the neuroscience of the brain, molecular interactions during protein folding, to the Internet. Evaluating network models on their ability to learn the underlying features automatically is integral to algorithm development for problem-solving in areas of machine learning to graph database system design. 

    Here we describe methods and develop algorithms that extend and evaluate hyperedge replacement grammars, a formalism in formal language theory. We detail extensions for model-inference on real-world networks and graph generation. Discovering patterns involved in system behavior to build models for real-world systems that preserve many of the network properties during the generation step is the central focus of this work. Growing similar structures at various scale are also crucial to the evolution of the scientific tools required in today's information landscape. Experimental results demonstrate that hyperedge replacement grammars offer a new way to learn network features that facilitate compelling graphical structure generation that advances network science in areas of modeling and network analysis.