Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities.

Identifying important nodes for disease spreading is a central topic in network epidemiology. We investigate how well the position of a node, characterized by standard network measures, can predict its epidemiological importance in any graph of a given number of nodes. This is in contrast to other studies that deal with the easier prediction problem of ranking nodes by their epidemic importance in given graphs. As a benchmark for epidemic importance, we calculate the exact expected outbreak size given a node as the source. We study exhaustively all graphs of a given size, so do not restrict ourselves to certain generative models for graphs, nor to graph data sets. Due to the large number of possible nonisomorphic graphs of a fixed size, we are limited to ten-node graphs. We find that combinations of two or more centralities are predictive (R2 scores of 0.91 or higher) even for the most difficult parameter values of the epidemic simulation. Typically, these successful combinations include one normalized spectral centrality (such as PageRank or Katz centrality) and one measure that is sensitive to the number of edges in the graph.

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications.

The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of textile-based systems, whereas the second one is one of the most used pressure sensitive insole (PSI) systems that is used as an alternative to smart socks. The proposed methods are reviewed for smart sock use in special medical applications, for gait and foot pressure analysis. The Pedar system is also shown, together with studies of validation and repeatability for Pedar and other in-shoe systems. Then, the applications of Pedar are presented, mainly in medicine and sports. Our purpose was to offer the researchers in this field a useful means to overview and select relevant information. Moreover, our review can be a starting point for new, relevant research towards improving the design and functionality of the systems, as well as extending the research towards other areas of applications using sensors in smart textiles and in-shoe systems.

Learning the mechanisms of network growth.

We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.

The network signature of constellation line figures.

In traditional astronomies across the world, groups of stars in the night sky were linked into constellations-symbolic representations rich in meaning and with practical roles. In some sky cultures, constellations are represented as line (or connect-the-dot) figures, which are spatial networks drawn over the fixed background of stars. We analyse 1802 line figures from 56 sky cultures spanning all continents, in terms of their network, spatial, and brightness features, and ask what associations exist between these visual features and culture type or sky region. First, an embedded map of constellations is learnt, to show clusters of line figures. We then form the network of constellations (as linked by their similarity), to study how similar cultures are by computing their assortativity (or homophily) over the network. Finally, we measure the diversity (or entropy) index for the set of constellations drawn per sky region. Our results show distinct types of line figures, and that many folk astronomies with oral traditions have widespread similarities in constellation design, which do not align with cultural ancestry. In a minority of sky regions, certain line designs appear universal, but this is not the norm: in the majority of sky regions, the line geometries are diverse.

Top influencers can be identified universally by combining classical centralities.

Information flow, opinion, and epidemics spread over structured networks. When using node centrality indicators to predict which nodes will be among the top influencers or superspreaders, no single centrality is a consistently good ranker across networks. We show that statistical classifiers using two or more centralities are instead consistently predictive over many diverse, static real-world topologies. Certain pairs of centralities cooperate particularly well in drawing the statistical boundary between the superspreaders and the rest: a local centrality measuring the size of a node's neighbourhood gains from the addition of a global centrality such as the eigenvector centrality, closeness, or the core number. Intuitively, this is because a local centrality may rank highly nodes which are located in locally dense, but globally peripheral regions of the network. The additional global centrality indicator guides the prediction towards more central regions. The superspreaders usually jointly maximise the values of both centralities. As a result of the interplay between centrality indicators, training classifiers with seven classical indicators leads to a nearly maximum average precision function (0.995) across the networks in this study.