@article {14426, title = {Higher-order graphical models for classification in social and affiliation networks}, journal = {NIPS 2010 Workshop on Networks Across Disciplines: Theory and Applications, Whistler BC, Canada}, year = {2010}, month = {2010///}, abstract = {In this work we explore the application of higher-order Markov Random Fields(MRF) to classification in social and affiliation networks. We consider both friend- ship links and group membership for inferring hidden attributes in a collective inference framework. We explore different ways of using the social groups as ei- ther node features or to construct the graphical model structure. The bottleneck in applying higher-order MRFs to a domain with many overlapping large cliques is the complexity of inference which is exponential in the size of the largest clique. To circumvent the slow inference problem, we borrow recent advancements in the computer vision community to achieve fast approximate inference results. We provide preliminary results using a dataset from facebook which suggest that our higher-order MRF models are capturing the structural dependencies in the net- works and they yield higher accuracy than linear classifiers. }, author = {Zheleva,E. and Getoor, Lise and Sarawagi,S.} }