New preprint: Community Detection with the Map Equation and Infomap: Theory and Applications https://t.co/zCk0pajzYp
Explore the map equation's theoretical framework and learn to apply Infomap to diverse research problems. @antoneri@daniel_edler@chrisbloecker
The new book 'Higher-order systems' by @lordgrilo and myself is now available here as part of the @springerpub series #UnderstandingComplexSystems:
https://t.co/7zlVgSNfPI
I can not thank enough our 56 contributors, who helped us put together 18 high-quality chapters...
Major release: Infomap 2.0 with regularized map equation – reveal flow-based communities in weighted, directed networks with incomplete data without overfitting.
pip install infomap
or
git clone [email protected]:mapequation/infomap.git
cd infomap
make -j
Our last work about integrating network structure and metadata using absorbing random walks and the map equation. Great collaboration with @m_rosvall@antoneri@AleixBassolas and Antoine Marot.
https://t.co/S0p90nmVel
Curious about non-local relations between metadata and modular structure in your networks? Preprint with @AleixBassolas, @antoneri, Antoine Marot, and Vincenzo Nicosia: Metadata-informed community detection with lazy encoding using absorbing random walks. https://t.co/PWyTfWmVnM
Trouble running Infomap in Windows? We now provide pre-compiled Infomap binaries for Windows. Download and run Infomap in the PowerShell. Feature request from many R users. Work by @antoneri and @daniel_edler. https://t.co/zyqdSDYJzc
In this paper @antoneri, @m_rosvall et al. define unipartite, bipartite, and multilayer network representations of hypergraph flows to extract the community structure of social and biological systems with higher-order interactions. @IceLab_umea https://t.co/2Ci1HNManP
Last news from the world of hypergraphs:
* "How choosing random-walk model and network representation matters for flow-based community detection in hypergraphs" by @antoneri@m_rosvall &al.
* "Hypergraph reconstruction from network data" by @_jgyou@lordgrilo & @tiagopeixoto
Now published in Communications Physics as
How choosing random-walk model and network representation matters for flow-based community detection in hypergraphs
with a new case study on metabolic networks. https://t.co/m1l6tIzH8S
@antoneri@daniel_edler @NetPaleo @manlius84
Today we released Infomap 1.4 with bug fixes and improved Python API, including NetworkX integration. For example, now you can export results to JSON (-o json). Upgrade with pip install -U infomap. https://t.co/675cfYh0ZO
Attending @Networks2021? Come join us at the Higher-Order Models in Network Science (HONS 2021) satellite on July 2nd! This year, we will focus on higher-order models in social systems and are looking forward to this program:
https://t.co/k0w5q9yQQd
How can we effectively simplify important structures in hypergraphs? We used Infomap to explore how different random-walk models and network representations change the number, size, depth, and overlap of identified multilevel communities. https://t.co/Shb5R1Mab5
Did you run enough attempts when you identified communities in your network? Check out our solution-landscape notebook and 1. identity the sufficient number of attempts 2. visualize the solution landscape 3. explore how solutions differ https://t.co/P6SSkwpgbU
Struggling to simplify and highlight important structures in large networks? We have released Infomap v1.0 with a new Python API, web worker, semantic versioning, and more. Check out https://t.co/jWkQeV3VWy and run Infomap without any installation!