Network Filtering for Big Data: Triangulated Maximally Filtered Graph

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 modelling. 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. Further, 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

Chordal Editing is Fixed-Parameter Tractable

Graph modification problems typically ask for a small set of operations that transforms a given graph to have a certain property. The most commonly considered operations include vertex deletion, edge deletion, and edge addition; for the same property, one can define significantly different versions by allowing different operations. We study a very general graph modification problem that allows all three types of operations: given a graph and integers k(1), k(2), and k(3), the CHORDAL EDITING problem asks whether G can be transformed into a chordal graph by at most k(1) vertex deletions, k(2) edge deletions, and k(3) edge additions. Clearly, this problem generalizes both CHORDAL DELETION and CHORDAL COMPLETION (also known as MINIMUM FILL-IN). Our main result is an algorithm for CHORDAL EDITING in time 2(O(klog k)). n(O(1)), where k:=k(1) + k(2) + k(3) and n is the number of vertices of G. Therefore, the problem is fixed-parameter tractable parameterized by the total number of allowed operations. Our algorithm is both more efficient and conceptually simpler than the previously known algorithm for the special case CHORDAL DELETION

Fast minimal triangulation algorithm using minimum degree criterion

AbstractWe propose an algorithm for minimal triangulation which, using simple and efficient strategy, subdivides the input graph in different, almost non-overlapping, subgraphs. Using the technique of matrix multiplication for saturating the minimal separators, we show that the partition of the graph can be computed in time O(nα) where nα is the time required by the binary matrix multiplication. After saturating the minimal separators, the same procedure is recursively applied on each subgraphs. We also present a variant of the algorithm in which the minimum degree criterion is used. In this way, we obtain an algorithm that uses minimum degree criterion and at the same time produces a minimal triangulation, thus shedding new light on the effectiveness of the minimum degree heuristics