中国物理B ›› 2017, Vol. 26 ›› Issue (7): 78901-078901.doi: 10.1088/1674-1056/26/7/078901

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇    下一篇

Feedback arcs and node hierarchy in directed networks

Jin-Hua Zhao(赵金华), Hai-Jun Zhou(周海军)   

  1. 1 Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China;
    2 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Department of Applied Science and Technology, Politecnico di Torino, 10129 Torino, Italy
  • 收稿日期:2017-05-11 修回日期:2017-05-27 出版日期:2017-07-05 发布日期:2017-07-05
  • 通讯作者: Hai-Jun Zhou E-mail:zhouhj@itp.ac.cn
  • 基金资助:

    Project by the National Basic Research Program of China (Grant No.2013CB932804) and the National Natural Science Foundations of China (Grant Nos.11121403 and 11225526).J-H Zhao also acknowledges support by Fondazione CRT under project SIBYL,initiative"La Ricerca dei Talenti".

Feedback arcs and node hierarchy in directed networks

Jin-Hua Zhao(赵金华)1,3, Hai-Jun Zhou(周海军)1,2   

  1. 1 Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China;
    2 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Department of Applied Science and Technology, Politecnico di Torino, 10129 Torino, Italy
  • Received:2017-05-11 Revised:2017-05-27 Online:2017-07-05 Published:2017-07-05
  • Contact: Hai-Jun Zhou E-mail:zhouhj@itp.ac.cn
  • Supported by:

    Project by the National Basic Research Program of China (Grant No.2013CB932804) and the National Natural Science Foundations of China (Grant Nos.11121403 and 11225526).J-H Zhao also acknowledges support by Fondazione CRT under project SIBYL,initiative"La Ricerca dei Talenti".

摘要:

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 leads to complex information-processing dynamics in the network and makes 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.

关键词: directed graph, feedback arc, hierarchy, message-passing algorithm

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 leads to complex information-processing dynamics in the network and makes 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.

Key words: directed graph, feedback arc, hierarchy, message-passing algorithm

中图分类号:  (Structures and organization in complex systems)

  • 89.75.Fb
75.10.Nr (Spin-glass and other random models) 87.16.Yc (Regulatory genetic and chemical networks) 02.70.Uu (Applications of Monte Carlo methods)