Coarsening Algorithm Based on Multi-Label Propagation for Knowledge Discovery in Bipartite Networks

Complex machine learning tasks for knowledge discovery in networked data, such as community detection, node categorization, network visualization, and dimension reduction, have been successfully addressed by coarsening algorithms. It iteratively reduces the original network into a hierarchy of smaller networks, resulting in informative simplifications of the original network at various degrees of detail. Few of these algorithms, however, have been specially built to cope with bipartite networks. Besides of this, current coarsening algorithms present the following theoretical limitations that should be addressed: 1) A high-cost search strategy in dense networks; 2) current coarsening algorithms are usually based on label propagation, which is limited to propagate single-labels; and 3) the synchronous label propagation scheme yields the cyclic oscillation problem. To overcome such limitations, we propose a coarsening algorithm based on multi-label propagation, which is more suitable for large-scale bipartite networks and allows a time-effective implementation. Furthermore, our proposal improves the standard semi-synchronous strategy and simultaneously propagates multiple labels to create the coarsened network representation. The empirical analysis of synthetic and real-world networks provides evidence that our coarsening strategy leads to significant gains regarding accuracy and runtime against standard techniques.