Click for a video demo of MNET-VR
Multilayer networks are an increasingly popular way to model complex relations between various types of entities and they have been applied to a large number of real-world data sets. Their intrinsic complexity makes the visualization of this type of network extremely challenging and still an open research area. To help the visual exploration of complex multilayer network structures, today we are releasing MNET-VR. MNET-VR is the output of a research project carried on with Leonard Maxim and supported by the Digital Design Department. MNET-VR explores the potential of Virtual Reality to visualize this type of network structure.
MNET-VR offers basic functions to visualize and filter multilayer network structures. MNET-VR does not offer, at this stage, the possibility to manipulate the network layout. A proper 3D layout of the network can be obtained through the R package multinet. While its primary goal is to explore multilayer networks, MNET-VR can also be used to visualize single layer networks using igraph and multinet. The export of of the files for visualization is done through two simple R functions that we make available on the website. MNET-VR is designed for Oculus Rift/Oculus Quest with Link.
We have published two new papers in December:
- The Node Vector Distance Problem in Complex Networks, by M. Coscia, A. Gomez-Lievano, F. Neffke, published in ACM Computing Surveys
The paper develops a new measure to quantify the distances between sets of nodes, with important applications on network dynamics such as spread of diseases.
Read Michele’s blog post about it: https://www.michelecoscia.com/?p=1898
- Data-driven strategies for optimal bicycle network growth, by L.G. Natera Orozco, F. Battiston, G. Iniguez, M. Szell, published in Royal Society Open Science
Here we investigate the network structure of bicycle networks in cities around the world, and find that they consist of hundreds of disconnected patches, even in cycling-friendly cities like Copenhagen. To connect these patches, we develop and apply data-driven, algorithmic network growth strategies, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks.
Today Roberta Sinatra received this year’s Complex Systems Society Junior Scientific Award!
The award was given for Roberta’s
pioneer contributions to the science of science and success, having had an impact in multiple fields, from network science to computational social science and scientometrics
The award is given once a year to two young CSS researchers, aimed at recognizing extraordinary scientific achievements: https://cssociety.org/community/css-awards