ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1706.04792
  4. Cited By
Mapping higher-order network flows in memory and multilayer networks
  with Infomap
v1v2 (latest)

Mapping higher-order network flows in memory and multilayer networks with Infomap

15 June 2017
Daniel Edler
Ludvig Bohlin
M. Rosvall
ArXiv (abs)PDFHTML

Papers citing "Mapping higher-order network flows in memory and multilayer networks with Infomap"

8 / 8 papers shown
Title
FlowHON: Representing Flow Fields Using Higher-Order Networks
FlowHON: Representing Flow Fields Using Higher-Order Networks
Nan Chen
Zhihong Li
Jun Tao
13
0
0
04 Dec 2023
The Map Equation Goes Neural: Mapping Network Flows with Graph Neural
  Networks
The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
Christopher Blöcker
Chester Tan
Ingo Scholtes
95
1
0
02 Oct 2023
Module-based regularization improves Gaussian graphical models when
  observing noisy data
Module-based regularization improves Gaussian graphical models when observing noisy data
Magnus Neuman
J. Calatayud
Viktor Tasselius
M. Rosvall
46
1
0
29 Mar 2023
Bayesian Detection of Mesoscale Structures in Pathway Data on Graphs
Bayesian Detection of Mesoscale Structures in Pathway Data on Graphs
Luka V. Petrović
Vincenzo Perri
44
0
0
16 Jan 2023
Cognitive modelling with multilayer networks: Insights, advancements and
  future challenges
Cognitive modelling with multilayer networks: Insights, advancements and future challenges
Massimo Stella
Salvatore Citraro
Giulio Rossetti
Daniele Marinazzo
Yoed N. Kenett
M. Vitevitch
75
1
0
02 Oct 2022
An adaptation of InfoMap to absorbing random walks using
  absorption-scaled graphs
An adaptation of InfoMap to absorbing random walks using absorption-scaled graphs
Esteban Vargas Bernal
M. A. Porter
J. Tien
56
0
0
21 Dec 2021
Predicting Influential Higher-Order Patterns in Temporal Network Data
Predicting Influential Higher-Order Patterns in Temporal Network Data
Christoph Gote
Vincenzo Perri
Ingo Scholtes
AI4TS
19
2
0
26 Jul 2021
The Minimum Description Length Principle for Pattern Mining: A Survey
The Minimum Description Length Principle for Pattern Mining: A Survey
E. Galbrun
AI4TS
80
24
0
28 Jul 2020
1