Computer Science, Rutgers
Learning and mining in large complex networks
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. These four publications should provide a nice background:
 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.
 Keith Henderson, Tina Eliassi-Rad, Spiros Papadimitriou, and Christos Faloutsos: HCDF: A Hybrid Community Discovery Framework. SIAM SDM 2010: 754-765.
 Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, and Christos Faloutsos: Using ghost edges for classification in sparsely labeled networks. ACM SIGKDD 2008: 256-264.
 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).
Feb. 17, 11:45, Wei Ji Ma. Optimality and Probabilistic Computation in Visual Categorization