Feedback arcs and node hierarchy in directed networks

Abstract

Directed networks such as gene regulation networks and neural networks are connected by arcs (directed links). The nodes in a directed network are often strongly interwound by a huge number of directed cycles, which lead to complex information-processing dynamics in the network and make it highly challenging to infer the intrinsic direction of information flow. In this theoretical paper, based on the principle of minimum-feedback, we explore the node hierarchy of directed networks and distinguish feedforward and feedback arcs. Nearly optimal node hierarchy solutions, which minimize the number of feedback arcs from lower-level nodes to higher-level nodes, are constructed by belief-propagation and simulated-annealing methods. For real-world networks, we quantify the extent of feedback scarcity by comparison with the ensemble of direction-randomized networks and identify the most important feedback arcs. Our methods are also useful for visualizing directed networks.

18 pages (main text and appendices, including 9 figures). This paper is related to and is an expansion of our previous post arXiv:1605.09257

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Cited by in corpus (3)

• Minimal Dominating Set problem studied by simulated annealing and cavity method: Analytics and population dynamics
• Optimal segmentation of directed graph and the minimum number of feedback arcs
• Belief propagation for permutations, rankings, and partial orders