Network Filtering for Big Data: Triangulated Maximally Filtered Graph

Understand

We propose a network-filtering method, the Triangulated Maximally Filtered Graph (TMFG), that provides an approximate solution to the Weighted Maximal Planar Graph problem.

• The underlying idea of TMFG consists in building a triangulation that maximizes a score function associated with the amount of information retained by the network.
• TMFG uses as weights any arbitrary similarity measure to arrange data into a meaningful network structure that can be used for clustering, community detection and modeling.
• The method is fast, adaptable and scalable to very large datasets, it allows online updating and learning as new data can be inserted and deleted with combinations of local and non-local moves.