Information parity in complex networks

Aline Viol* (Corresponding Author), Vesna Vuksanovic, Philipp Hoevel* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


A growing interest in complex networks theory results in an ongoing demand for new analytical tools. We propose a novel quantity based on information theory that provides a new perspective for a better understanding of networked systems: Termed ”information parity”, it quantifies the consonance of influence among nodes with respect to the whole network architecture. Considering the statistics of geodesic distances, information parity assesses how similarly a pair of nodes can influence and be influenced by the network. This allows us to quantify the access of information gathered by the nodes. To demonstrate the method's potential, we evaluate a social network and human brain networks. Our results indicate that emerging phenomena like an ideological orientation of nodes in social networks can be detected by their information parities. We also show the potential of information parity to identify central network regions in structural brain networks placed near mid-sagittal plane. We find that functional networks have, on average, greater information parity for inter-hemispheric homologous regions in comparison to the whole network. This property of information parity suggests that the functional correlations between regional activities could be explained by the symmetry of their overall influences on the whole brain.

Original languageEnglish
Article number125233
Number of pages9
JournalPhysica. A, Statistical Mechanics and its Applications
Early online date8 Sept 2020
Publication statusPublished - 1 Jan 2021

Bibliographical note

The authors acknowledge support by Deutsche Forschungsgemeinschaft under Grant No. HO4695/3-1 and within the framework of Collaborative Research Center 910. The authors thank Danielle Bassett for providing anatomical networks data. AV thank Gandhi Viswanathan and Jorge Ruiz for discussions.


  • complex networks
  • topology
  • information theory
  • symmetry
  • brain networks
  • social networks
  • Brain networks
  • Complex networks
  • Social networks
  • Topology
  • Information theory
  • Symmetry
  • GAME


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