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The power of motif counting: Theory, Algorithms, and Applications for Large Graphs

Nesreen K. Ahmed
Senior Research Scientist, Intel Research Labs

The recent interest in analyzing complex systems in various domains has fueled a large body of research on both algorithms and models that capture the latent structure of large data. Graphs arise as a natural representation of data in many domains, such as social, biological, and information domains. Numerous applications often rely on higher-order information such as motifs for intuitive and meaningful characterization of graphs. However, the extent of how useful they are for analyzing and modeling graphs is not well understood. In this talk, I will discuss our recent work on theory, algorithms, and applications for large-scale motif counting, and outline how motifs can be used to learn more accurate deep learning models for graphs. 

Nesreen Ahmed is a senior research scientist at Intel Labs. She received her Ph.D. from the Computer Science Department at Purdue University in 2015, and her M.S. in statistics and computer science from Purdue University in 2014. In 2018, she is the PC Co-Chair of the IEEE Big Data Conference. Dr. Ahmed was a visiting researcher at Facebook, Adobe research, Technicolor, and Intel analytics. Her research interests in machine learning and data mining spans the theory and algorithms of large-scale machine learning, graph theory, and their applications in social and information networks. Dr. Ahmed has authored numerous papers/tutorials in top-tier conferences/journals. Her research was selected among the best papers of ICDM in 2015, BigMine in 2012, and covered by popular press such as the MIT Technology Review. She was selected by UC Berkeley among the top female rising stars in computer science and engineering in 2014. Dr. Ahmed holds 2 U.S. patents filed by Adobe research, and co-founded the open source network data repository (