"Layer Communities in Multiplex Networks"

The final coordinates of one of my papers finally appeared with its journal coordinates, although a published version was already available in August 2017. It just took a while for the full special issue in which it appeared to be published, so only now to do we have our volume, issue, and page numbers. This is my first publication in Journal of Statistical Physics.

Here are some other details:

Title: Layer Communities in Multiplex Networks

Authors: Ta-Chu Kao and Mason A. Porter

Abstract: Multiplex networks are a type of multilayer network in which entities are connected to each other via multiple types of connections. We propose a method, based on computing pairwise similarities between layers and then doing community detection, for grouping structurally similar layers in multiplex networks. We illustrate our approach using both synthetic and empirical networks, and we are able to find meaningful groups of layers in both cases. For example, we find that airlines that are based in similar geographic locations tend to be grouped together in a multiplex airline network and that related research areas in physics tend to be grouped together in a multiplex collaboration network.

"Detection of Core–Periphery Structure in Networks Using Spectral Methods and Geodesic Paths"

The special issue in European Journal of Applied Mathematics on "Network Analysis and Modelling" that I co-edited also includes a research paper that I coauthored. Here are some details about that paper.

Title: Detection of Core–Periphery Structure in Networks Using Spectral Methods and Geodesic Paths

Authors: Mihai Cucuringu, Puck Rombach, Sang Hoon Lee, and Mason A. Porter

Abstract: We introduce several novel and computationally efficient methods for detecting "core–periphery structure" in networks. Core–periphery structure is a type of mesoscale structure that consists of densely connected core vertices and sparsely connected peripheral vertices. Core vertices tend to be well-connected both among themselves and to peripheral vertices, which tend not to be well-connected to other vertices. Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that that vertex is a core vertex. Our second method is based on a low-rank approximation of a network’s adjacency matrix, which we express as a perturbation of a tensor-product matrix. Our third approach uses the bottom eigenvector of the random-walk Laplacian to infer a coreness score and a classification into core and peripheral vertices. We also design an objective function to (1) help classify vertices into core or peripheral vertices and (2) provide a goodness-of-fit criterion for classifications into core versus peripheral vertices. To examine the performance of our methods, we apply our algorithms to both synthetically generated networks and a variety of networks constructed from real-world data sets.

Tales from the ArXiv: Iterated Agony Edition

The last sentence of this paper is priceless: We analytically characterize the detectability threshold and we show that an iterated version of agony can partly overcome this resolution limit.

A Fine-Scale, Historically Awesome, Genetic Map of the U.K.

A new genetic map of the U.K., which is the product of several-years-long collaboration between geneticists, historians, and others. This looks really neat, and I recommend looking at the map at the other end of the link.

The first line of the news article articulates how cool this is: A remarkable new map of Britain shows how the nation was forged by successive waves of immigration from continental Europe over 10,000 years since the end of the last Ice Age.

Again quoting the article: Geneticists and historians collaborated closely on the 10-years project and have been astonished to find that patterns in the DNA of Britons living today reflect historical events going back centuries, and in some cases millennia. Take a look in the news article for some specific examples.

I am sure that there is a ton more than one can do with the data---and of course, I wonder how much one can improve on the analysis (and, without having looked with seriousness at the Nature article, naturally I have no scientific opinion on its correctness). Many of the coauthors are my colleagues at Oxford, so maybe I will eventually have a chance to play with the data? :)

Update: Taking a quick look at the paper, one could certainly try to use more sophisticated clustering methods. Whether that would have any effect on the qualitative conclusions remains to be seen.