Higher-Order Aggregate Networks in the Analysis of Temporal Networks: Path structures and centralities

Recent research on temporal networks has highlighted the limitations of a static network perspective for our understanding of complex systems with dynamic topologies. In particular, recent works have shown that i) the specific order in which links occur in real-world temporal networks affects causality structures and thus the evolution of dynamical processes, and ii) higher-order aggregate representations of temporal networks can be used to analytically study the effect of these order correlations on dynamical processes. In this article we analyze the effect of order correlations on path-based centrality measures in real-world temporal networks. Analyzing temporal equivalents of betweenness, closeness and reach centrality in six empirical temporal networks, we first show that an analysis of the commonly used static, time-aggregated representation can give misleading results about the actual importance of nodes. We further study higher-order time-aggregated networks, a recently proposed generalization of the commonly applied static, time-aggregated representation of temporal networks. Here, we particularly define path-based centrality measures based on second-order aggregate networks, empirically validating that node centralities calculated in this way better capture the true temporal centralities of nodes than node centralities calculated based on the commonly used static (first-order) representation. Apart from providing a simple and practical method for the approximation of path-based centralities in temporal networks, our results highlight interesting perspectives for the use of higher-order aggregate networks in the analysis of time-stamped network data.Comment: 27 pages, 13 figures, 3 table

An ensemble perspective on multi-layer networks

We study properties of multi-layered, interconnected networks from an ensemble perspective, i.e. we analyze ensembles of multi-layer networks that share similar aggregate characteristics. Using a diffusive process that evolves on a multi-layer network, we analyze how the speed of diffusion depends on the aggregate characteristics of both intra- and inter-layer connectivity. Through a block-matrix model representing the distinct layers, we construct transition matrices of random walkers on multi-layer networks, and estimate expected properties of multi-layer networks using a mean-field approach. In addition, we quantify and explore conditions on the link topology that allow to estimate the ensemble average by only considering aggregate statistics of the layers. Our approach can be used when only partial information is available, like it is usually the case for real-world multi-layer complex systems

Quantifying and suppressing ranking bias in a large citation network

It is widely recognized that citation counts for papers from different fields cannot be directly compared because different scientific fields adopt different citation practices. Citation counts are also strongly biased by paper age since older papers had more time to attract citations. Various procedures aim at suppressing these biases and give rise to new normalized indicators, such as the relative citation count. We use a large citation dataset from Microsoft Academic Graph and a new statistical framework based on the Mahalanobis distance to show that the rankings by well known indicators, including the relative citation count and Google's PageRank score, are significantly biased by paper field and age. Our statistical framework to assess ranking bias allows us to exactly quantify the contributions of each individual field to the overall bias of a given ranking. We propose a general normalization procedure motivated by the z-score which produces much less biased rankings when applied to citation count and PageRank score

Primary familial brain calcification: Genetic analysis and clinical spectrum

BackgroundPrimary familial brain calcification (PFBC) is a rare autosomal dominant disorder with bilateral calcification of basal ganglia and other cerebral regions, movement disorders, and neuropsychiatric disturbances. So far, three causative genes have been discovered: SLC20A2, PDGFRB and PDGFB, accounting for approximately 50% of cases. MethodsSeven unrelated families with primary brain calcification were recruited to undergo clinical and genetic analysis, including Sanger sequencing of SLC20A2, PDGFRB, and PDGFB, and copy number analysis of SLC20A2. ResultsMutations in SLC20A2 have been detected in three families: p.Glu368Glyfs*46, p.Ser434Trp, and p.Thr595Met. Intrafamilial phenotype variability has been observed. In spite of this, we found similar neuroimaging pattern among members of the same family. ConclusionsThis molecular analysis expands the mutational spectrum of SLC20A2, which remains the major causative gene of primary familial brain calcification, and suggests the existence of disease-causing mutations in at least another, still unknown gene. (c) 2014 International Parkinson and Movement Disorder Societ