Country as Network
Places = Nodes; Paths = Edges; look!
The spaces are full.
Tingari Story, 1986
Willy Tjungurrayi
https://www.artgallery.nsw.gov.au/collection/works/548.1993/
#art #haiku #ArtViaHaiku
#Australia #aboriginal
#networkscience #netsci #math
A study combines methods from network science, information theory, and cognitive science to examine the information present in note transitions within music composed by J. S. Bach. It identifies and explains differences in information content across various compositional forms based on their network structure.
Network memory impacts dynamical processes emerging in real-world social systems, however little is known about memory of temporal networks beyond pairwise interactions. The authors develop a framework to characterize the temporal organization of higher-order networks and propose a model of temporal hypergraphs with higher-order memory to reproduce the patterns emerging in real-world complex systems.
Congratulations to @sara_picorana for winning the @EuroVis PhD award for her dissertation "Layered graphs and their layouts, evaluations, and applications" 🎉 H/t advisor @codydunne
https://repository.library.northeastern.edu/files/neu:4f196k78t
#NetSci #NetworkScience #GraphTheory #DataVis #HCI #Graphs #Math
Graphs are a fundamental data structure, used in many fields of human knowledge work. We read, use, and categorize information through graphs in daily tasks: every time we read a map of public transportation, every time we create a mental map of a concept, every time we send and receive packets through the internet, we are using graphs. Given how widespread graphs are, it is fundamental that we visualize them effectively. Drawings of graphs should strive to respect important readability criteria that were established in previous research. Examples of such criteria include reducing the number of crossings between edges and reducing edge length. Here, graph layout algorithms come into play: each layout algorithm maps nodes and edges in a graph to coordinates in space, allowing us to draw a graph while attempting to respect our previously-mentioned readability criteria. We encounter graphs with a wide variety of features and users with diverse use cases: layout algorithms must take into account this breadth of possible requirements, and specialize accordingly. This thesis focuses on layered graphs-graphs in which each node is assigned to a layer, nodes belonging to the same layer are drawn aligned to a linear axis, and the axes for multiple layers are drawn in parallel. The bulk of the novel contributions of this thesis are found in Stratisfimal Layout, which is an optimal layout algorithm for layered graphs-meaning that its output is one of the best attainable in terms of selected readability criteria. It does so by defining the layout algorithm as a linear programming problem. The other works in this thesis explore heuristic-based approaches to highlight trends and patterns in the underlying data, the application of layout algorithms to hypergraphs, and how the shape of the layout influences the understanding of features in the data.--Author's abstract
Join us for the 1st British #NetSci Symposium in London!
May 30: Public talk by Vittoria Colizza @ Northeastern.
May 31: Workshop @ Queen Mary. Morning talks & afternoon discussion panels across academia, industry & publishing.
*PhD students & postdocs working on #networks #networkscience, apply to give a talk!*
Details: www.netsci.uk/events.html
Question for #netsci folks since maybe I'm looking in the wrong places or using the wrong language.
Has there been any conversation or synthesis between the exponential random graph model statistical community and the graph embedding ML community?
I used @birb to create a followable RSS feed for Nicola Perra’s netscience account: now on Bluesky it was run on Twitter as a pure information account that simply highlights (with a one sentence summary) interesting new work (typically preprints) in network science. A really great service!
to receive Nicola’s posts, follow this account
@bsky.app.profile.netscience.bsky.social
for more on Nicola Perra himself see https://www.nicolaperra.com
Lisette Espín-Noboa
Cynthia Siew
Gerardo Iñiguez
Jason H. Moore, Ph.D.
Khoury Vis Lab @ Northeastern
Eleanor Power
Brian C. Keegan, Ph.D.
Ulrike Hahn
