De-evolution of Preferential Attachment Trees

Chen Avin, Yuri Lotker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Given a graph Gt which is a result of a t time, evolutionary process, the goal of graph de-evolution of Gt is to infer what was the structure of the graph Gt′ for t< t. This general inference problem is very important for understanding the mechanisms behind complex systems like social networks and their asymptotic behavior. In this work we take a step in this direction and consider undirected, unlabeled trees that are the result of the well known random preferential attachment process. We compute the most likely root set (possible isomorphic patient zero candidates) of the tree, as well as the most likely previous graph Gt - 1 structure. While the one step forward reasoning in preferential attachment is very simple, the backward (past) reasoning is more complex and includes the use of the automorphism and isomorphism of graphs, which we elucidate here.

Original languageAmerican English
Title of host publicationComplex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
EditorsRosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, Marta Sales-Pardo
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030653507
StatePublished - 1 Jan 2021
Event9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020 - Madrid, Spain
Duration: 1 Dec 20203 Dec 2020

Publication series

NameStudies in Computational Intelligence


Conference9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020


  • De-evolution
  • Evolution
  • Preferential attachment
  • Social networks
  • Time
  • Trees

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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