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Learning and Mining in Large Complex Networks

Dr. Tina Eliassi-Rad

Monday, April 18, 2011, 12:00pm - 07:00pm

Rutgers University, Department of Computer Science

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Complex networks are ubiquitous in many domains. Examples include technological, informational, social, and biological networks.  In this talk, I will present algorithms for both relational classification and clustering in such networked data.  I will pay special attention to issues surrounding scalability, transfer of knowledge across networks, and evaluation in such non-IID settings.

Background reading is available at http://eliassi.org/pubs.html">http://eliassi.org/pubs.html.  These four publications should provide a nice background:

[1] Jennifer Neville, Brian Gallagher, Tina Eliassi-Rad, and Tao Wang: Correcting Evaluation Bias of Relational Classifiers with Network Cross Validation. Knowledge and Information Systems (KAIS), Springer, January 2011.
http://eliassi.org/papers/neville-kais11.pdf">http://eliassi.org/papers/neville-kais11.pdf

[2] Keith Henderson, Tina Eliassi-Rad, Spiros Papadimitriou, and Christos Faloutsos: HCDF: A Hybrid Community Discovery Framework. SIAM SDM 2010: 754-765.
http://eliassi.org/papers/henderson-sdm10.pdf">http://eliassi.org/papers/henderson-sdm10.pdf

[3] Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, and Christos Faloutsos: Using ghost edges for classification in sparsely labeled networks. ACM SIGKDD 2008: 256-264.
http://eliassi.org/papers/gallagher-kdd08.pdf">http://eliassi.org/papers/gallagher-kdd08.pdf

[4] Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad: Collective Classification in Network Data. AI Magazine 29(3): 93-106 (2008).
http://eliassi.org/papers/ai-mag-tr08.pdf">http://eliassi.org/papers/ai-mag-tr08.pdf

Dr. Tina Eliassi-Rad