Wednesday, November 13, 2019
You can read ESPN's summary of this week's Major Baseball Awards on this page.
Tuesday, November 12, 2019
ESPN is tabulate this week's award winners on this page.
Monday, November 11, 2019
Take a look at this page for a tabulation of Major League Baseball's 2019 awards. Today's announcement of the Rookies of the Year kicks off a week of pronouncements. The three finalists for each award were announced previously.
Update: The vote tallies are available at this page.
Sunday, November 10, 2019
The sort of paper title that one ultimately regrets, I think. pic.twitter.com/g19H4yTdn7— Stuart Ritchie (@StuartJRitchie) November 10, 2019
(Tip of the cap to Chris Marcum.)
Saturday, November 09, 2019
In case you ever wanted to see the Karate Club network visualized using a capuccino embedding.— Mason Porter (@masonporter) November 10, 2019
(Thanks to University of Michigan postdoc Sofia Piltz for trying this out for me at BeanBerry Cafe in Ann Arbor. She'll try it with a higher-contrast and more circular picture later.) pic.twitter.com/nqLpcJrfmY
Sofia found this picture in one of Petter Holme's presentations, although it reminds me of one of them from old papers and t-shirt designs.
Wednesday, November 06, 2019
Monday, November 04, 2019
Titles: Supracentrality Analysis of Temporal Networks with Directed Interlayer Coupling
Authors: Dane Taylor, Mason A. Porter, and Peter J. Mucha
Abstract: We describe centralities in temporal networks using a supracentrality framework to study centrality trajectories, which characterize how the importances of nodes change in time. We study supracentrality generalizations of eigenvector-based centralities, a family of centrality measures for time-independent networks that includes PageRank, hub and authority scores, and eigenvector centrality. We start with a sequence of adjacency matrices, each of which represents a time layer of a network at a different point or interval of time. Coupling centrality matrices across time layers with weighted interlayer edges yields a supracentrality matrix ℂ(𝜔), where ω controls the extent to which centrality trajectories change over time. We can flexibly tune the weight and topology of the interlayer coupling to cater to different scientific applications. The entries of the dominant eigenvector of ℂ(𝜔) represent joint centralities, which simultaneously quantify the importance of every node in every time layer. Inspired by probability theory, we also compute marginal and conditional centralities. We illustrate how to adjust the coupling between time layers to tune the extent to which nodes’ centrality trajectories are influenced by the oldest and newest time layers. We support our findings by analysis in the limits of small and large ω.