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arXiv2018

About Network Structures and Systems Complexity

This paper provides the analysis for structural and functional approaches of complex network systems research. In order to study the behavior of these systems the flow adjacency matrices were introduced, and local and global dynamic characteristics of system elements were defined. The notion of the flow core of network system was introduced and its advantages over the k-core of complex network were analyzed. The methods were proposed for identifying real structure of network system and reducing the dimensionality of its model.

physics.soc-ph↗

arXiv2018

Unveiling Scholarly Communities over Knowledge Graphs

Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. Exploiting semantics encoded in knowledge graphs enables the implementation of knowledge-driven tasks such as semantic retrieval, query processing, and question answering, as well as solutions to knowledge discovery tasks including pattern discovery and link prediction. In this paper, we tackle the problem of knowledge discovery in scholarly knowledge graphs, i.e., graphs that integrate scholarly data, and present Korona, a knowledge-driven framework able to unveil scholarly communities for the prediction of scholarly networks. Korona implements a graph partition approach and relies on semantic similarity measures to determine relatedness between scholarly entities. As a proof of concept, we built a scholarly knowledge graph with data from researchers, conferences, and papers of the Semantic Web area, and apply Korona to uncover co-authorship networks. Results observed from our empirical evaluation suggest that exploiting semantics in scholarly knowledge graphs enables the identification of previously unknown relations between researchers. By extending the ontology, these observations can be generalized to other scholarly entities, e.g., articles or institutions, for the prediction of other scholarly patterns, e.g., co-citations or academic collaboration.

cs.DL↗

arXiv2018

Time-Bounded Influence Diffusion with Incentives

A widely studied model of influence diffusion in social networks represents the network as a graph $G=(V,E)$ with an influence threshold $t(v)$ for each node. Initially the members of an initial set $S\subseteq V$ are influenced. During each subsequent round, the set of influenced nodes is augmented by including every node $v$ that has at least $t(v)$ previously influenced neighbours. The general problem is to find a small initial set that influences the whole network. In this paper we extend this model by using \emph{incentives} to reduce the thresholds of some nodes. The goal is to minimize the total of the incentives required to ensure that the process completes within a given number of rounds. The problem is hard to approximate in general networks. We present polynomial-time algorithms for paths, trees, and complete networks.

cs.DS↗

arXiv2018

Emergent Democracy

This essay argues that a new form of democracy - an "Emergent Democracy" - will develop as a result of the use of Internet communication tools and platforms such as blogs. The essay explores a variety of tools available and explores the history of democracy, modern experiments with democracy and how these tools might support democracy. The essay also explores concerns as these new tools emerge. These issues include concerns such as privacy and the societally negative use of these tools by corporations, totalitarian regimes and terrorists.

physics.soc-ph↗

arXiv2018

Datamining a medieval medical text reveals patterns in ingredient choice that reflect biological activity against the causative agents of specified infections

The pharmacopeia used by physicians and lay people in medieval Europe has largely been dismissed as placebo or superstition. While we now recognise that some of the materia medica used by medieval physicians could have had useful biological properties, research in this area is limited by the labour-intensive process of searching and interpreting historical medical texts. Here, we demonstrate the potential power of turning medieval medical texts into contextualised electronic databases amenable to exploration by algorithm. We use established methodologies from network science to reveal statistically significant patterns in ingredient selection and usage in a key text, the fifteenth-century Lylye of Medicynes, focusing on remedies to treat symptoms of microbial infection. We discuss the potential that these patterns reflect rational medical decisions. In providing a worked example of data-driven textual analysis, we demonstrate the potential of this approach to encourage interdisciplinary collaboration and to shine a new light on the ethnopharmacology of historical medical texts.

q-bio.QM↗

arXiv2018

Inventor collaboration and its persistence across European regions

Inventor collaborations that span across regions facilitate the combination and diffusion of innovation-related knowledge. While remote partnerships are gaining importance; most R&D cooperations remain embedded in local environments and little is known about how spatial patterns of creation and persistence of ties in large-scale co-inventor networks differ. We use the publicly available OECD REGPAT database to construct a co-inventor network of the EU27 and continental EFTA countries from patents filed between 2006 and 2010, and identify those ties that had been persisted from before 2006. A community detection exercise reveals that persisted collaborations cluster at a smaller geographical scale than what is observed in the full network. We find that in general the estimated probability of persisted collaboration does not differ from the complete collaboration network when geographical distance, technological similarity or the number of common third partners across regions are assumed to influence tie probability separately. However, persistent collaboration gains exceptional likelihood if regions are proximate in all three dimensions. Our results therefore offer evidence that repeated inventor collaboration drives regional innovation systems towards isolation, which is a threat for European innovation policy.

physics.soc-ph↗

arXiv2018

Self-regulation promotes cooperation in social networks

Cooperative behavior in real social dilemmas is often perceived as a phenomenon emerging from norms and punishment. To overcome this paradigm, we highlight the interplay between the influence of social networks on individuals, and the activation of spontaneous self-regulating mechanisms, which may lead them to behave cooperatively, while interacting with others and taking conflicting decisions over time. By extending Evolutionary game theory over networks, we prove that cooperation partially or fully emerges whether self-regulating mechanisms are sufficiently stronger than social pressure. Interestingly, even few cooperative individuals act as catalyzing agents for the cooperation of others, thus activating a recruiting mechanism, eventually driving the whole population to cooperate.

physics.soc-ph↗

arXiv2018

Complex Economic Activities Concentrate in Large Cities

Why do some economic activities agglomerate more than others? And, why does the agglomeration of some economic activities continue to increase despite recent developments in communication and transportation technologies? In this paper, we present evidence that complex economic activities concentrate more in large cities. We find this to be true for technologies, scientific publications, industries, and occupations. Using historical patent data, we show that the urban concentration of complex economic activities has been continuously increasing since 1850. These findings suggest that the increasing urban concentration of jobs and innovation might be a consequence of the growing complexity of the economy.

physics.soc-ph↗

arXiv2018

Maximum-Likelihood Network Reconstruction for SIS Processes is NP-Hard

The knowledge of the network topology is imperative to precisely describing the viral dynamics of an SIS epidemic process. In scenarios for which the network topology is unknown, one resorts to reconstructing the network from observing the viral state trace. This work focusses on the impact of the viral state observations on the computational complexity of the resulting network reconstruction problem. We propose a novel method of constructing a specific class of viral state traces from which the inference of the presence or absence of links is either easy or difficult. In particular, we use this construction to prove that the maximum-likelihood SIS network reconstruction is NP-hard. The NP-hardness holds for any adjacency matrix of a graph which is connected.

cs.CC↗

arXiv2018

General Research Framework and its Properties Analysis of Traffic Flow Evolutionary Dynamics

This paper aims to the conditions of traffic flow evolving to stability and the stability of equilibrium under demand time-varying of traffic networks. The general framework of the evolution of flow dynamics by adopting evolutionary game theory and dynamic system stability theory is proposed. The multi group and multi criteria travel choice flow evolution dynamic model is carried out.

physics.soc-ph↗

arXiv2018

Emergent Properties of Terrorist Networks, Percolation and Social Narrative

In this paper, we have initiated an attempt to develop and understand the driving mechanisms that underlie fourth-generation warfare. We have undertaken this from a perspective of endeavoring to understand the drivers of these events from a Complexity perspective by using a threshold-type percolation model. We propose to integrate this strategic level model with tactical level Big Data, behavioral, statistical projections via a fractal operational level model and to construct a hierarchical framework that allows dynamic prediction. Our initial study concentrates on this strategic level, i.e. a percolation model. Our main conclusion from this initial study is that extremist terrorist events are not solely driven by the size of a supporting population within a socio-geographical location but rather a combination of ideological factors that also depends upon the involvement of the host population. This involvement, through the social, political and psychological fabric of society, not only contributes to the active participation of terrorists within society but also directly contributes to and increases the likelihood of the occurrence of terrorist events. Our calculations demonstrate the links between Islamic extremist terrorist events, the ideologies within the Muslim and non-Muslim population that facilitates these terrorist events (such as Anti-Zionism) and anti-Semitic occurrences of violence against the Jewish population. In a future paper, we hope to extend the work undertaken to construct a predictive model and extend our calculations to other forms of terrorism such as Right Wing fundamentalist terrorist events within the USA.

physics.soc-ph↗

arXiv2018

Graph Distance from the Topological View of Non-backtracking Cycles

Whether comparing networks to each other or to random expectation, measuring dissimilarity is essential to understanding the complex phenomena under study. However, determining the structural dissimilarity between networks is an ill-defined problem, as there is no canonical way to compare two networks. Indeed, many of the existing approaches for network comparison differ in their heuristics, efficiency, interpretability, and theoretical soundness. Thus, having a notion of distance that is built on theoretically robust first principles and that is interpretable with respect to features ubiquitous in complex networks would allow for a meaningful comparison between different networks. Here we introduce a theoretically sound and efficient new measure of graph distance, based on the "length spectrum" function from algebraic topology, which compares the structure of two undirected, unweighted graphs by considering their non-backtracking cycles. We show how this distance relates to structural features such as presence of hubs and triangles through the behavior of the eigenvalues of the so-called non-backtracking matrix, and we showcase its ability to discriminate between networks in both real and synthetic data sets. By taking a topological interpretation of non-backtracking cycles, this work presents a novel application of Topological Data Analysis to the study of complex networks.

cs.SI↗

arXiv2018

Data-Oriented Algorithm for Real-Time Estimation of Flow Rates and Flow Directions in a Water Distribution Network

The aim of this paper is to present how data collected from a water distribution network (WDN) can be used to reconstruct flow rate and flow direction all over the network to enhance knowledge and detection of unforeseen events. The methodological approach consists in modeling the WDN and all available sensor data related to the management of such a network in the form of a flow network graph G = (V, E, s, t, c), with V a set of nodes, E a set of edges whose elements are ordered pairs of distinct nodes, s a source node, t a sink node and c a capacity function on edges. Our objective is to reconstruct a real-valued function f(u,v): VxV => R on all the edges E in VxV from partial observations on a small number of nodes V = {1, ..., n}. This reconstruction method consists in a data-driven Ford-Fulkerson maximum-flow problem in a multi-source, multi-sink context using a constrained bidirectional breadth-first search based on Edmonds-Karp method. The innovative approach is its application in the context of smart cities to operate from sensor data, structural data from a geographical information system (GIS) and consumption estimates.

physics.soc-ph↗

arXiv2018

Social Connection Induces Cultural Contraction: Evidence from Hyperbolic Embeddings of Social and Semantic Networks

Research has repeatedly demonstrated the influence of social connection and communication on convergence in cultural tastes, opinions and ideas. Here we review recent studies and consider the implications of social connection on cultural, epistemological and ideological contraction, then formalize these intuitions within the language of information theory. To systematically examine connectivity and cultural diversity, we introduce new methods of manifold learning to map both social networks and topic combinations into comparable, two-dimensional hyperbolic spaces or Poincaré disks, which represent both hierarchy and diversity within a system. On a Poincaré disk, radius from center traces the position of an actor in a social hierarchy or an idea in a cultural hierarchy. The angle of the disk required to inscribe connected actors or ideas captures their diversity. Using this method in the epistemic culture of 21st Century physics, we empirically demonstrate that denser university collaborations systematically contract the space of topics discussed and ideas investigated more than shared topics drive collaboration, despite the extreme commitments academic physicists make to research programs over the course of their careers. Dense connections unleash flows of communication that contract otherwise fragmented semantic spaces into convergent hubs or polarized clusters. We theorize the dynamic interplay between structural expansion and cultural contraction and explore how this introduces an essential tension between the enjoyment and protection of difference.

physics.soc-ph↗

arXiv2021

Preferences for COVID-19 vaccine: Evidence from India

India's mass vaccination efforts have been slow due to high levels of vaccine hesitancy. This study uses data from an online discrete choice experiment with 1371 respondents to rigorously examine the factors shaping vaccine preference in the country. We find that vaccine efficacy, presence of side effects, protection duration, distance to vaccination centre and vaccination rates within social networks play a critical role in determining vaccine demand. We apply a non-parametric model to uncover the heterogeneity in the effects of these factors. We derive two novel insights from this analysis. First, even though, on average, domestically developed vaccines are preferred, around 30 percent of the sample favours foreign-developed vaccines. Second, the vaccine preference of around 15 percent of the sample is highly sensitive to the presence of side effects and vaccination uptake among their peer group. These results provide insights into the ongoing policy debate around vaccine adoption in India.

physics.soc-ph↗

arXiv2021

From centre to centres: polycentric structures in individual mobility

The availability of large-scale datasets collected via mobile phones has opened up opportunities to study human mobility at an individual level. The granular nature of these datasets calls for the design of summary statistics that can be used to describe succinctly mobility patterns. In this work, we show that the radius of gyration, a popular summary statistic to quantify the extent of an individual's whereabouts, suffers from a sensitivity to outliers, and is incapable of capturing mobility organised around multiple centres. We propose a natural generalisation of the radius of gyration to a polycentric setting, as well as a novel metric to assess the quality of its description. With these notions, we propose a method to identify the centres in an individual's mobility and apply it to two large mobility datasets with socio-demographic features, showing that a polycentric description can capture features that a monocentric model is incapable of.

physics.soc-ph↗

arXiv2021

Dodgeball -- Can a Satellite Avoid Being Hit by Debris?

Can a satellite dodge a collision with untracked orbiting debris? Can a satellite dodge collision with a tracked object, making only the avoidance manœuvers actually required to avoid collision, despite the uncertainties of predicted conjunctions? Satellite-borne radar may distinguish actual collision threats from the much greater number of near misses because an object on a collision course has constant bearing, which may be determined by interferometric detection of the radar return. A large constellation of such radars may enable the determination of the ephemerides of all cm-sized debris in LEO.

astro-ph.EP↗

arXiv2021

Accessibility: A Generalization of the Node Degree (A Tutorial)

Robust and comprehensive characterization of the topological properties of complex networks requires the adoption of several respective measurements, among which the node degree has special importance. In the present work, we provide an introduction to one of these measurements, namely the accessibility of a node, which can be understood as a generalization of the concept of node degree not only to incorporate successive neighborhoods of that node, but also to reflect specific types of dynamics unfolding in the network. After discussing the node degree and its hierarchical extension, we present the concepts of random walk, entropy, and then the accessibility. Several examples of its numeric calculation are provided, as well as some experimental results indicating that it can effectively complement the information provided by other topological measurements of four types of complex networks, namely Erdos-Renyi, Watts-Strogatz, Barabasi-Albert, and Geometric. We also describe how a recently developed toolbox can be used for the calculation of accessibility in relatively large networks.

physics.soc-ph↗