Classification over bipartite graphs through projection

Many real-world large datasets correspond to bipartite graph data settings; think for example of users rating movies or people visiting locations. Although some work exists over such bigraphs, no general network-oriented methodology has been proposed yet to perform node classification. In this paper we propose a three-stage classification framework that effectively deals with the typical very large size of such datasets. First, a weighting of the top nodes is defined. Secondly, the bigraph is projected into a unipartite (homogenous) graph among the bottom nodes, where the weights of the edges are a function of the weights of the top nodes in the bigraph. Finally, relational learners/classiers are applied to the resulting weighted unigraph. This general framework allows us to explore the design space, by applying different choices for the three stages, introducing new alternatives and mixing-and-matching to create new techniques. We present an empirical study of the predictive and run-time performances for different combinations of functions in the three stages over a large collection of bipartite datasets. There are clear differences in predictive performance with different design choices. Based on these results, we propose several specic combinations that show good accuracy and also allow for easy and fast scaling to big datasets. A comparison with a linear SVM method on the adjacency matrix of the bigraph shows the superiority of the network-oriented approach.