An application of neighbourhoods in digraphs to the classification of binary dynamics

Pedro Vitor Rodrigues Da Conceicao, Dejan Govc, Janis Lazovskis, Ran Levi* (Corresponding Author), Henri Riihimaki, Jason P Smith

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


A binary state on a graph means an assignment of binary values to its vertices. A time-dependent sequence of binary states is referred to as binary dynamics. We describe a method for the classification of binary dynamics of digraphs, using particular choices of closed neighbourhoods. Our motivation and application comes from neuroscience, where a directed graph is an abstraction of neurons and their connections, and where the simplification of large amounts of data is key to any computation. We present a topological/graph theoretic method for extracting information out of binary dynamics on a graph, based on a selection of a relatively small number of vertices and their neighbourhoods. We consider existing and introduce new real-valued functions on closed neighbourhoods, comparing them by their ability to accurately classify different binary dynamics. We describe a classification algorithm that uses two parameters and sets up a machine learning pipeline. We demonstrate the effectiveness of the method on simulated activity on a digital reconstruction of cortical tissue of a rat, and on a nonbiological random graph with similar density.

Original languageEnglish
Pages (from-to)528-551
Number of pages24
JournalNetwork Neuroscience
Issue number2
Early online date2 Mar 2022
Publication statusPublished - 1 Jun 2022


  • binary dynamics
  • directed graphs
  • graph and topological parameters
  • neural networks
  • signal classification
  • Graph and topological parameters
  • Neural networks
  • Directed graphs
  • Binary dynamics
  • Signal classification


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