Network Filtering for Big Data: Triangulated Maximally Filtered Graph
This provides a scalable network-filtering solution for big data analysis, though it appears incremental as an approximate method building on existing graph problems.
The authors tackled the problem of network filtering for big data by proposing the Triangulated Maximally Filtered Graph (TMFG), which approximates the Weighted Maximal Planar Graph to retain meaningful information efficiently, resulting in a fast, scalable method suitable for clustering and community detection.
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. TMFG permits readjustments of the network in consequence of changes in the strength of the similarity measure. The method is based on local topological moves and can therefore take advantage of parallel and GPUs computing. We discuss how this network-filtering method can be used intuitively and efficiently for big data studies and its significance from an information-theoretic perspective.