Motif-h: a novel functional backbone extraction for directed networks

Abstract

Dense networks are very pervasive in social analytics, biometrics, communication, architecture, etc. Analyzing and visualizing such large-scale networks are significant challenges, which are generally met by reducing the redundancy on the level of nodes or edges. Motifs, patterns of the higher order organization compared with nodes and edges, are recently found to be the novel fundamental unit structures of complex networks. In this work, we proposed a novel motif h-backbone (Motif-h) method to extract functional cores of directed networks based on both motif strength and h-bridge. Compared with the state-of-the-art method Motif-DF and Entropy, our method solves two main issues which are often found in existing methods: the Motif-h reconsiders weak ties into our candidate set, and those weak ties often have critical functions of bridges in networks; moreover, our method provides a trade-off between the motif size and the edge strength, which quantifies the core edges accordingly. In the simulations, we compare our method with Motif-DF in four real-world networks and found that Motif-h can streamline the extraction of crucial structures compared with the others with limited edges.

An improved voterank algorithm to identifying a set of influential spreaders in complex networks

“…Along with the rapid development of information technology, high-speed networking is increasingly important to human society, and systems in many fields are abstracted as complex networks for research [1,2]. Many aspects of our lives are covered by a large number of complex networks, such as power networks [3], transportation networks [4], and trade networks [5].…”