Communication now and then : analyzing the Republic of Letters as a communication network

Huge advances in understanding patterns of human communication, and the underlying social networks where it takes place, have been made recently using massive automatically recorded data sets from digital communication, such as emails and phone calls. However, it is not clear to what extent these results on human behaviour are artefacts of contemporary communication technology and culture and if the fundamental patterns in communication have changed over history. This paper presents an analysis of historical epistolary metadata with the aim of comparing the underlying historical communication patterns with those of contemporary communication. Our work uses a new epistolary dataset containing metadata on over 150,000 letters sent between the 16th and 19th centuries. The analyses indicate striking resemblances between contemporary and epistolary communication network patterns, including dyadic interactions and ego-level behaviour. Certain aspects of the letter datasets are insufficient to corroborate other similarities or differences for these communication networks. Despite these drawbacks, our work helps confirm that several features of human communication are not artefacts of contemporary mediums or culture, but are likely elements of human behaviour.Peer reviewe

Unifying the Framework of Multi-Layer Network and Visual Analytics

International audienceThe notion of multi-layer networks introduces a general framework and common vocabulary for existing ideas in complex network theory. In doing so, it is possible to understand and compare these dierent ideas in a new and more fruitful manner. However, to make this operationalizable to the visualization and visual analytics community, we need more clarity. For example: What is a layer? What are the semantics of interlayer edges, and specifically, identity links between layers? Can dierent multilayered networks be expressed or implemented in the same way? And vice versa, can one multilayered network be expressed or implemented in dierent ways

Isomorphisms in Multilayer Networks

We extend the concept of graph isomorphisms to multilayer networks with any number of "aspects" (i.e., types of layering). In developing this generalization, we identify multiple types of isomorphisms. For example, in multilayer networks with a single aspect, permuting vertex labels, layer labels, and both vertex labels and layer labels each yield different isomorphism relations between multilayer networks. Multilayer network isomorphisms lead naturally to defining isomorphisms in any of the numerous types of networks that can be represented as a multilayer network, and we thereby obtain isomorphisms for multiplex networks, temporal networks, networks with both of these features, and more. We reduce each of the multilayer network isomorphism problems to a graph isomorphism problem, where the size of the graph isomorphism problem grows linearly with the size of the multilayer network isomorphism problem. One can thus use software that has been developed to solve graph isomorphism problems as a practical means for solving multilayer network isomorphism problems. Our theory lays a foundation for extending many network analysis methods - including motifs, graphlets, structural roles, and network alignment - to any multilayer network.Peer reviewe

EDENetworks: A user-friendly software to build and analyse networks in biogeography, ecology and population genetics

The recent application of graph-based network theory analysis to biogeography, community ecology and population genetics has created a need for user-friendly software, which would allow a wider accessibility to and adaptation of these methods. EDENetworks aims to fill this void by providing an easy-to-use interface for the whole analysis pipeline of ecological and evolutionary networks starting from matrices of species distributions, genotypes, bacterial OTUs or populations characterized genetically. The user can choose between several different ecological distance metrics, such as Bray-Curtis or Sorensen distance, or population genetic metrics such as FST or Goldstein distances, to turn the raw data into a distance/dissimilarity matrix. This matrix is then transformed into a network by manual or automatic thresholding based on percolation theory or by building the minimum spanning tree. The networks can be visualized along with auxiliary data and analysed with various metrics such as degree, clustering coefficient, assortativity and betweenness centrality. The statistical significance of the results can be estimated either by resampling the original biological data or by null models based on permutations of the data

Efficient limited-time reachability estimation in temporal networks

Time-limited states characterize many dynamical processes on networks: disease-infected individuals recover after some time, people forget news spreading on social networks, or passengers may not wait forever for a connection. These dynamics can be described as limited-waiting-time processes, and they are particularly important for systems modeled as temporal networks. These processes have been studied via simulations, which is equivalent to repeatedly finding all limited-waiting-time temporal paths from a source node and time. We propose a method yielding an orders-of-magnitude more efficient way of tracking the reachability of such temporal paths. Our method gives simultaneous estimates of the in- or out-reachability (with any chosen waiting-time limit) from every possible starting point and time. It works on very large temporal networks with hundreds of millions of events on current commodity computing hardware. This opens up the possibility to analyze reachability and dynamics of spreading processes on large temporal networks in completely new ways. For example, one can now compute centralities based on global reachability for all events or can find with high probability the infected node and time, which would lead to the largest epidemic outbreak.Peer reviewe

Tracking the cumulative knowledge spreading in a comprehensive citation network

In all of science, the authors of publications depend on the knowledge presented by the previous publications. Thus they "stand on the shoulders of giants" and there is a flow of knowledge from previous publications to more recent ones. The dominating paradigm for tracking this flow of knowledge is to count the number of direct citations, but this neglects the fact that beneath the first layer of citations there is a full body of literature. In this study, we go underneath the "shoulders" by investigating the cumulative knowledge creation process in a citation network of around 35 million publications. In particular, we study stylized models of persistent influence and diffusion that take into account all the possible chains of citations. When we study the persistent influence values of publications and their citation counts, we find that the publications related to Nobel prizes, i.e., Nobel papers have higher ranks in terms of persistent influence than that due to citations, and that the most outperforming publications are typically early works leading to hot research topics of their time. The diffusion model reveals a significant variation in the rates at which different fields of research share knowledge. We find that these rates have been increasing systematically for several decades, which can be explained by the increase in the publication volumes. Overall, our results suggest that analyzing cumulative knowledge creation on a global scale can be useful in estimating the type and scale of scientific influence of individual publications and entire research areas as well as yielding insights that could not be discovered by using only the direct citation counts.Peer reviewe

The Russian invasion of Ukraine selectively depolarized the Finnish NATO discussion on Twitter

It is often thought that an external threat increases the internal cohesion of a nation, and thus decreases polarization. We examine this proposition by analyzing NATO discussion dynamics on Finnish social media following the Russian invasion of Ukraine in February 2022. In Finland, public opinion on joining the North Atlantic Treaty Organization (NATO) had long been polarized along the left-right partisan axis, but the invasion led to a rapid convergence of opinion toward joining NATO. We investigate whether and how this depolarization took place among polarized actors on Finnish Twitter. By analyzing retweet patterns, we find three separate user groups before the invasion: a pro-NATO, a left-wing anti-NATO, and a conspiracy-charged anti-NATO group. After the invasion, the left-wing anti-NATO group members broke out of their retweeting bubble and connected with the pro-NATO group despite their difference in partisanship, while the conspiracy-charged anti-NATO group mostly remained a separate cluster. Our content analysis reveals that the left-wing anti-NATO group and the pro-NATO group were bridged by a shared condemnation of Russia's actions and shared democratic norms, while the other anti-NATO group, mainly built around conspiracy theories and disinformation, consistently demonstrated a clear anti-NATO attitude. We show that an external threat can bridge partisan divides in issues linked to the threat, but bubbles upheld by conspiracy theories and disinformation may persist even under dramatic external threats.Peer reviewe

Communication now and then : analyzing the Republic of Letters as a communication network

Huge advances in understanding patterns of human communication, and the underlying social networks where it takes place, have been made recently using massive automatically recorded data sets from digital communication, such as emails and phone calls. However, it is not clear to what extent these results on human behaviour are artefacts of contemporary communication technology and culture and if the fundamental patterns in communication have changed over history. This paper presents an analysis of historical epistolary metadata with the aim of comparing the underlying historical communication patterns with those of contemporary communication. Our work uses a new epistolary dataset containing metadata on over 150,000 letters sent between the 16th and 19th centuries. The analyses indicate striking resemblances between contemporary and epistolary communication network patterns, including dyadic interactions and ego-level behaviour. Certain aspects of the letter datasets are insufficient to corroborate other similarities or differences for these communication networks. Despite these drawbacks, our work helps confirm that several features of human communication are not artefacts of contemporary mediums or culture, but are likely elements of human behaviour.Peer reviewe