"Random-Graph Models and Characterization of Granular Networks"

A paper of mine from 2020 now has its final coordinates listed on the published file itself. Here are some details.

Title: Random-Graph Models and Characterization of Granular Networks

Authors: Silvia Nauer, Lucas Böttcher, and Mason A. Porter

Abstract: Various approaches and measures from network analysis have been applied to granular and particulate networks to gain insights into their structural, transport, failure-propagation and other systems-level properties. In this article, we examine a variety of common network measures and study their ability to characterize various two-dimensional and three-dimensional spatial random-graph models and empirical two-dimensional granular networks. We identify network measures that are able to distinguish between physically plausible and unphysical spatial network models. Our results also suggest that there are significant differences in the distributions of certain network measures in two and three dimensions, hinting at important differences that we also expect to arise in experimental granular networks.

"Spatial Strength Centrality and the Effect of Spatial Embeddings on Network Architecture"

One of my papers came out in final form today. Here is a link to the paper, and here are some details.

Title: "Spatial Strength Centrality and the Effect of Spatial Embeddings on Network Architecture"

Authors: Andrew Liu and Mason A. Porter

Abstract: For many networks, it is useful to think of their nodes as being embedded in a latent space, and such embeddings can affect the probabilities for nodes to be adjacent to each other. In this paper, we extend existing models of synthetic networks to spatial network models by first embedding nodes in Euclidean space and then modifying the models so that progressively longer edges occur with progressively smaller probabilities. We start by extending a geographical fitness model by employing Gaussian-distributed fitnesses, and we then develop spatial versions of preferential attachment and configuration models. We define a notion of “spatial strength centrality” to help characterize how strongly a spatial embedding affects network structure, and we examine spatial strength centrality on a variety of real and synthetic networks.

"Topological Data Analysis of Continuum Percolation with Disks"

One of my papers came out in final form today. Here are some details and a story.

Title: Topological Data Analysis of Continuum Percolation with Disks

Authors: Leo Speidel, Heather A. Harrington, S. Jonathan Chapman, and Mason A. Porter

Abstract: We study continuum percolation with disks, a variant of continuum percolation in two-dimensional Euclidean space, by applying tools from topological data analysis. We interpret each realization of continuum percolation with disks as a topological subspace of [0,1]^2 and investigate its topological features across many realizations. Specifically, we apply persistent homology to investigate topological changes as we vary the number and radius of disks, and we observe evidence that the longest persisting invariant is born at or near the percolation transition.


And to give a story, or at least the hint of the interesting relationship that I sometimes have with typesetters and editors, here is a note that I received from them while we were working on the galley proofs.

Update (8/05/18): A nice way of phrasing things is that we're in a nonassociative situation, and hyphens are a great tool to indicate exactly (and tersely) where the parentheses should be to group terms in a way that renders their meaning unambiguous. (And, naturally, if somebody makes a change in my text that I don't like, my immediate desire is to change it back.)

"Neither Global nor Local: Heterogeneous Connectivity in Spatial Network Structures of World Migration"

One of my papers, which has had a DOI for about half a year, finally has its final publication coordinates. Notably, this is my first paper in a sociology journal. Here are some details.

Title: Neither Global nor Local: Heterogeneous Connectivity in Spatial Network Structures of World Migration

Authors: Valentin Danchev and Mason A. Porterc

Abstract: For a long time, geographic regions were considered the dominant spatial arbiter of international migration of people. However, since the late 1970s, many scholars have argued that movements reach beyond contiguous regions to connect distant, dispersed, and previously disconnected countries across the globe. The precise structure of world migration, however, remains an open question. We apply network analysis that incorporates spatial information to international migration-stock data to examine what multilateral structures of world migration have emerged from the interplay of regional concentration (local cohesion)and global interconnectedness (global cohesion) for the period 1960–2000. In the world migration network (WMN), nodes represent countries located in geographic space, and edges represent migrants froman origin country who live in a destination country during each decade. We characterize the large-scale structure and evolution of the WMN by algorithmically detecting international migration communities (i.e., sets of countries that are densely connected via migration) using a generalized modularity function for spatial, temporal, and directed networks. Our findings for the whole network suggest that movements in the WMN deviate significantly from the regional boundaries of the world and that international migration communities have become globally interconnected over time. However, we observe a strong variability in the distribution of strengths, neighborhood overlaps, and lengths of migration edges in the WMN. This manifests as three types of communities: global, local, and glocal. We find that long-distance movements in global communities bridge multiple non-contiguous countries, whereas local (and, to a lesser extent, glocal) communities remain trapped in contiguous geographic regions (or neighboring regions) for almost the whole period, contributing to a spatially fragmented WMN. Our findings demonstrate that world migration is neither regionally concentrated nor globally interconnected, but instead exhibits a heterogeneous connectivity pattern that channels unequal migration opportunities across the world.

"Mean-Field Approach to Evolving Spatial Networks, with an Application to Osteocyte Network Formation"

One of my papers came out in final form today. Here are the details.

Title: Mean-Field Approach to Evolving Spatial Networks, with an Application to Osteocyte Network Formation

Authors: Jake P. Taylor-King, David Basanta, S. Jonathan Chapman, and Mason A. Porter

Abstract: We consider evolving networks in which each node can have various associated properties (a state) in addition to those that arise from network structure. For example, each node can have a spatial location and a velocity, or it can have some more abstract internal property that describes something like a social trait. Edges between nodes are created and destroyed, and new nodes enter the system.We introduce a "local state degree distribution" (LSDD) as the degree distribution at a particular point in state space. We then make a mean-field assumption and thereby derive an integro-partial differential equation that is satisfied by the LSDD. We perform numerical experiments and find good agreement between solutions of the integro-differential equation and the LSDD from stochastic simulations of the full model. To illustrate our theory, we apply it to a simple model for osteocyte network formation within bones, with a view to understanding changes that may take place during cancer. Our results suggest that increased rates of differentiation lead to higher densities of osteocytes, but with a smaller number of dendrites. To help provide biological context, we also include an introduction to osteocytes, the formation of osteocyte networks, and the role of osteocytes in bone metastasis.

Subway Maps Compared to their Actual Geographies

Here are some cool visualizations of subway maps morphing to their actual geographies.

And if you want to learn about the amount of information to navigate such things, you may be interested in this paper.

(Tip of the cap to Marta González.)

"Mesoscale Analyses of Fungal Networks as an Approach for Quantifying Phenotypic Traits"

Another of my papers just came out in final form, and in fact it appears consecutively with another of my papers. It too has been available for quite a while in the journal, but we finally have our coordinates (page numbers, etc.).

Along with this paper, we have released a large data set of fungal networks. We hope that you enjoy playing with the data!

Here are some more details.

Title: "Mesoscale Analyses of Fungal Networks as an Approach for Quantifying Phenotypic Traits"

Authors: Sang Hoon Lee, Mark D. Fricker, and Mason A. Porter

Abstract: We investigate the application of mesoscopic response functions (MRFs) to characterize a large set of networks of fungi and slime moulds grown under a wide variety of different experimental treatments, including inter-species competition and attack by fungivores. We construct 'structural networks' by estimating cord conductances (which yield edge weights) from the experimental data, and we construct 'functional networks' by calculating edge weights based on how much nutrient traffic is predicted to occur along each edge. Both types of networks have the same topology, and we compute MRFs for both families of networks to illustrate two different ways of constructing taxonomies to group the networks into clusters of related fungi and slime moulds. Although both network taxonomies generate intuitively sensible groupings of networks across species, treatments and laboratories, we find that clustering using the functional-network measure appears to give groups with lower intra-group variation in species or treatments. We argue that MRFs provide a useful quantitative analysis of network behaviour that can (1) help summarize an expanding set of increasingly complex biological networks and (2) help extract information that captures subtle changes in intra- and inter-specific phenotypic traits that are integral to a mechanistic understanding of fungal behaviour and ecology. As an accompaniment to our paper, we also make a large data set of fungal networks available in the public domain.

"Null Models for Community Detection in Spatially Embedded, Temporal Networks"

Another one of my papers finally got its volume, issue, and page numbers last week. (It came out in advanced access in November 2015.) I finally got my own copy of the document today, so here are some details.

Title: Null Models for Community Detection in Spatially Embedded, Temporal Networks

Authors: Marta Sarzynska, Elizabeth A. Leicht, Gerardo Chowell, and Mason A. Porter

Abstract: In the study of networks, it is often insightful to use algorithms to determine mesoscale features such as 'community structure', in which densely connected sets of nodes constitute 'communities' that have sparse connections to other communities. The most popular way of detecting communities algorithmically is to maximize the quality function known as modularity. When maximizing modularity, one compares the actual connections in a (static or time-dependent) network to the connections obtained from a random-graph ensemble that acts as a null model. The communities are then the sets of nodes that are connected to each other densely relative to what is expected from the null model. Clearly, the process of community detection depends fundamentally on the choice of the null model, so it is important to develop and analyse novel null models that take into account appropriate features of the system under study. In this paper, we investigate the effects of using null models that incorporate spatial information, and we propose a novel null model based on the radiation model of population spread. We also develop novel synthetic spatial benchmark networks in which the connections between entities are based on the distance or flux between nodes, and we compare the performance of static and time-dependent versions of the radiation null model to the standard ('Newman–Girvan') null model for modularity optimization and to a recently proposed gravity null model. In our comparisons, we use both the above synthetic benchmarks and time-dependent correlation networks that we construct using countrywide dengue fever incidence data for Peru. Our findings illustrate the need to use appropriate generative models for the development of spatial null models for community detection.

Visualization of Stores in Cities

Take a look at these visualizations of cities with every store mapped. Very cool!

Somebody should do some network analysis of this stuff. :)

(Tip of the cap to Stacy Kerkela.)

Freeway Knot Theory

Some help from knot theorists would be appreciated. Here is a photoshopped picture of a freeway (based on a real one in Oakland, California) that needs to be untangled.

(Tip of the cap to Stanley Somers.)

Note: The original version of my post described the picture incorrectly as being an actual freeway. See this link. Also, this freeway is knotted up even more severely. :)