Graph Spectral Filtering for Network Simplification

Visualization is an important tool in the analysis and understanding of networks and their content. However, visualization tools face major challenges when dealing with large networks, mainly due to visual clutter. In this context, network simplification has been a main alternative to handle massive networks, reducing complexity while preserving relevant patterns of the network structure and content. In this paper we propose a methodology that rely on Graph Signal Processing theory to filter multivariate data associated to network nodes, assisting and enhancing network simplification and visualization tasks. The simplification process takes into account both topological and multivariate data associated to network nodes to create a hierarchical representation of the network. The effectiveness of the proposed methodology is assessed through a comprehensive set of quantitative evaluation and comparisons, which gauge the impact of the proposed filtering process in the simplification and visualization tasks.