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arXiv2013

Graph Metrics for Temporal Networks

Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node-node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.

physics.soc-ph↗

arXiv2013

Applications of Temporal Graph Metrics to Real-World Networks

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.

physics.soc-ph↗

arXiv2013

Data Leak Aware Crowdsourcing in Social Network

Harnessing human computation for solving complex problems call spawns the issue of finding the unknown competitive group of solvers. In this paper, we propose an approach called Friendlysourcing to build up teams from social network answering a business call, all the while avoiding partial solution disclosure to competitive groups. The contributions of this paper include (i) a clustering based approach for discovering collaborative and competitive team in social network (ii) a Markov-chain based algorithm for discovering implicit interactions in the social network.

cs.SI↗

arXiv2012

Experimental study of pedestrian flow through a T-junction

In this study, series of experiments under laboratory conditions were carried out to investigate pedestrian flow through a T-junction, i.e., two branches merging into the main stream. The whole duration of the experiments was recorded by video cameras and the trajectories of each pedestrian were extracted using the software Petrack from these videos. The Voronoi method is used to resolve the fine structure of the fundamental diagram and spatial dependence of the measured quantities from trajectories. In our study, only the data in the stationary state are used by analyzing the time series of density and velocity. The density, velocity and specific flow profiles are obtained by refining the size of the measurement area (here 10 cm \times 10 cm are adopted). With such a high resolution, the spatial distribution of density, velocity and specific flow can be obtained separately and the regions with higher value can be observed intuitively. Finally, the fundamental diagrams of T-junction flow is compared in three different locations. It is shown that the fundamental diagrams of the two branches match well. However, the velocities in front of the merging are significantly lower than that in the main stream at the same densities. After the merging, the specific flow increases with the density ρtill 2.5 m-2. While in the branches, the specific flow is almost independent of the density between ρ= 1.5 m-2 and 3.5 m-2

physics.soc-ph↗

arXiv2014

Distributed Generation and Resilience in Power Grids

We study the effects of the allocation of distributed generation on the resilience of power grids. We find that an unconstrained allocation and growth of the distributed generation can drive a power grid beyond its design parameters. In order to overcome such a problem, we propose a topological algorithm derived from the field of Complex Networks to allocate distributed generation sources in an existing power grid.

physics.soc-ph↗

arXiv2012

On the distribution of time-to-proof of mathematical conjectures

What is the productivity of Science? Can we measure an evolution of the production of mathematicians over history? Can we predict the waiting time till the proof of a challenging conjecture such as the P-versus-NP problem? Motivated by these questions, we revisit a suggestion published recently and debated in the "New Scientist" that the historical distribution of time-to-proof's, i.e., of waiting times between formulation of a mathematical conjecture and its proof, can be quantified and gives meaningful insights in the future development of still open conjectures. We find however evidence that the mathematical process of creation is too much non-stationary, with too little data and constraints, to allow for a meaningful conclusion. In particular, the approximate unsteady exponential growth of human population, and arguably that of mathematicians, essentially hides the true distribution. Another issue is the incompleteness of the dataset available. In conclusion we cannot really reject the simplest model of an exponential rate of conjecture proof with a rate of 0.01/year for the dataset that we have studied, translating into an average waiting time to proof of 100 years. We hope that the presented methodology, combining the mathematics of recurrent processes, linking proved and still open conjectures, with different empirical constraints, will be useful for other similar investigations probing the productivity associated with mankind growth and creativity.

physics.soc-ph↗

arXiv2011

Epidemics on contact networks: a general stochastic approach

Dynamics on networks is considered from the perspective of Markov stochastic processes. We partially describe the state of the system through network motifs and infer any missing data using the available information. This versatile approach is especially well adapted for modelling spreading processes and/or population dynamics. In particular, the generality of our systematic framework and the fact that its assumptions are explicitly stated suggests that it could be used as a common ground for comparing existing epidemics models too complex for direct comparison, such as agent-based computer simulations. We provide many examples for the special cases of susceptible-infectious-susceptible (SIS) and susceptible-infectious-removed (SIR) dynamics (e.g., epidemics propagation) and we observe multiple situations where accurate results may be obtained at low computational cost. Our perspective reveals a subtle balance between the complex requirements of a realistic model and its basic assumptions.

physics.soc-ph↗

arXiv2022

Incentives, lockdown, and testing: from Thucydides's analysis to the COVID-19 pandemic

In this work, we provide a general mathematical formalism to study the optimal control of an epidemic, such as the COVID-19 pandemic, via incentives to lockdown and testing. In particular, we model the interplay between the government and the population as a principal-agent problem with moral hazard, à la Cvitanić, Possamaï, and Touzi [27], while an epidemic is spreading according to dynamics given by compartmental stochastic SIS or SIR models, as proposed respectively by Gray, Greenhalgh, Hu, Mao, and Pan [45] and Tornatore, Buccellato, and Vetro [88]. More precisely, to limit the spread of a virus, the population can decrease the transmission rate of the disease by reducing interactions between individuals. However, this effort, which cannot be perfectly monitored by the government, comes at social and monetary cost for the population. To mitigate this cost, and thus encourage the lockdown of the population, the government can put in place an incentive policy, in the form of a tax or subsidy. In addition, the government may also implement a testing policy in order to know more precisely the spread of the epidemic within the country, and to isolate infected individuals. In terms of technical results, we demonstrate the optimal form of the tax, indexed on the proportion of infected individuals, as well as the optimal effort of the population, namely the transmission rate chosen in response to this tax. The government's optimisation problem then boils down to solving an Hamilton-Jacobi-Bellman equation. Numerical results confirm that if a tax policy is implemented, the population is encouraged to significantly reduce its interactions. If the government also adjusts its testing policy, less effort is required on the population side, individuals can interact almost as usual, and the epidemic is largely contained by the targeted isolation of positively-tested individuals.

q-bio.PE↗

arXiv2020

Paid and hypothetical time preferences are the same: Lab, field and online evidence

The use of hypothetical instead of real decision-making incentives remains under debate after decades of economic experiments. Standard incentivized experiments involve substantial monetary costs due to participants' earnings and often logistic costs as well. In time preferences experiments, which involve future payments, real payments are particularly problematic. Since immediate rewards frequently have lower transaction costs than delayed rewards in experimental tasks, among other issues, (quasi)hyperbolic functional forms cannot be accurately estimated. What if hypothetical payments provide accurate data which, moreover, avoid transaction cost problems? In this paper, we test whether the use of hypothetical - versus real - payments affects the elicitation of short-term and long-term discounting in a standard multiple price list task. One-out-of-ten participants probabilistic payment schemes are also considered. We analyze data from three studies: a lab experiment in Spain, a well-powered field experiment in Nigeria, and an online extension focused on probabilistic payments. Our results indicate that paid and hypothetical time preferences are mostly the same and, therefore, that hypothetical rewards are a good alternative to real rewards. However, our data suggest that probabilistic payments are not.

physics.soc-ph↗

arXiv2013

Overlapping modularity at the critical point of k-clique percolation

One of the most remarkable social phenomena is the formation of communities in social networks corresponding to families, friendship circles, work teams, etc. Since people usually belong to several different communities at the same time, the induced overlaps result in an extremely complicated web of the communities themselves. Thus, uncovering the intricate community structure of social networks is a non-trivial task with great potential for practical applications, gaining a notable interest in the recent years. The Clique Percolation Method (CPM) is one of the earliest overlapping community finding methods, which was already used in the analysis of several different social networks. In this approach the communities correspond to k-clique percolation clusters, and the general heuristic for setting the parameters of the method is to tune the system just below the critical point of k-clique percolation. However, this rule is based on simple physical principles and its validity was never subject to quantitative analysis. Here we examine the quality of the partitioning in the vicinity of the critical point using recently introduced overlapping modularity measures. According to our results on real social- and other networks, the overlapping modularities show a maximum close to the critical point, justifying the original criteria for the optimal parameter settings.

physics.soc-ph↗

arXiv2012

Opinion dynamics with disagreement and modulated information

Opinion dynamics concerns social processes through which populations or groups of individuals agree or disagree on specific issues. As such, modelling opinion dynamics represents an important research area that has been progressively acquiring relevance in many different domains. Existing approaches have mostly represented opinions through discrete binary or continuous variables by exploring a whole panoply of cases: e.g. independence, noise, external effects, multiple issues. In most of these cases the crucial ingredient is an attractive dynamics through which similar or similar enough agents get closer. Only rarely the possibility of explicit disagreement has been taken into account (i.e., the possibility for a repulsive interaction among individuals' opinions), and mostly for discrete or 1-dimensional opinions, through the introduction of additional model parameters. Here we introduce a new model of opinion formation, which focuses on the interplay between the possibility of explicit disagreement, modulated in a self-consistent way by the existing opinions' overlaps between the interacting individuals, and the effect of external information on the system. Opinions are modelled as a vector of continuous variables related to multiple possible choices for an issue. Information can be modulated to account for promoting multiple possible choices. Numerical results show that extreme information results in segregation and has a limited effect on the population, while milder messages have better success and a cohesion effect. Additionally, the initial condition plays an important role, with the population forming one or multiple clusters based on the initial average similarity between individuals, with a transition point depending on the number of opinion choices.

physics.soc-ph↗

arXiv2013

Searchability of central nodes in networks

Social networks are discrete systems with a large amount of heterogeneity among nodes (individuals). Measures of centrality aim at a quantification of nodes' importance for structure and function. Here we ask to which extent the most central nodes can be found by purely local search. We find that many networks have close-to-optimal searchability under eigenvector centrality, outperforming searches for degree and betweenness. Searchability of the strongest spreaders in epidemic dynamics tends to be substantially larger for supercritical than for subcritical spreading.

physics.soc-ph↗

arXiv2014

The network source location problem: ground state energy, entropy and effects of freezing

Ground state entropy of the network source location problem is evaluated at both the replica symmetric level and one-step replica symmetry breaking level using the entropic cavity method. The regime that is a focus of this study, is closely related to the vertex cover problem with randomly quenched covered nodes. The resulting entropic message passing inspired decimation and reinforcement algorithms are used to identify the optimal location of sources in single instances of transportation networks. The conventional belief propagation without taking the entropic effect into account is also compared. We find that in the glassy phase the entropic message passing inspired decimation yields a lower ground state energy compared to the belief propagation without taking the entropic effect. Using the extremal optimization algorithm, we study the ground state energy and the fraction of frozen hubs, and extend the algorithm to collect statistics of the entropy. The theoretical results are compared with the extremal optimization results.

cond-mat.dis-nn↗

arXiv2014

Stochastic Dynamics of the Multi-State Voter Model over a Network based on Interacting Cliques and Zealot Candidates

The stochastic dynamics of the multi-state voter model is investigated on a class of complex networks made of non-overlapping cliques, each hosting a political candidate and interacting with the others via Erdős-Rényi links. Numerical simulations of the model are interpreted in terms of an ad-hoc mean field theory, specifically tuned to resolve the inter/intra-clique interactions. Under a proper definition of the thermodynamic limit (with the average degree of the agents kept fixed while increasing the network size), the model is found to display the empirical scaling discovered by Fortunato and Castellano (Phys Rev Lett 99(13):138701, 2007), while the vote distribution resembles roughly that observed in Brazilian elections.

physics.soc-ph↗

arXiv2017

From Ecology to Finance (and Back?): Recent Advancements in the Analysis of Bipartite Networks

Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analysis of their topological structures has revealed the ubiquitous presence of properties which seem to characterize many - apparently different - systems. Nestedness, for example, has been observed in plants-pollinator as well as in country-product trade networks. This has raised questions about the significance of these patterns, which are often believed to constitute a genuine signature of self-organization. Here, we review several methods that have been developed for the analysis of such evidence. Due to the interdisciplinary character of complex networks, tools developed in one field, for example ecology, can greatly enrich other areas of research, such as economy and finance, and vice versa. With this in mind, we briefly review several entropy-based bipartite null models that have been recently proposed and discuss their application to several real-world systems. The focus on these models is motivated by the fact that they show three very desirable features: analytical character, general applicability and versatility. In this respect, entropy-based methods have been proven to perform satisfactorily both in providing benchmarks for testing evidence-based null hypotheses and in reconstructing unknown network configurations from partial information. On top of that, entropy-based models have been successfully employed to analyze ecological as well as economic systems, thus representing an ideal, interdisciplinary tool to approach the study of bipartite complex systems. [...]

physics.soc-ph↗

arXiv2017

Tackling information asymmetry in networks: a new entropy-based ranking index

Information is a valuable asset for agents in socio-economic systems, a significant part of the information being entailed into the very network of connections between agents. The different interlinkages patterns that agents establish may, in fact, lead to asymmetries in the knowledge of the network structure; since this entails a different ability of quantifying relevant systemic properties (e.g. the risk of financial contagion in a network of liabilities), agents capable of providing a better estimate of (otherwise) unaccessible network properties, ultimately have a competitive advantage. In this paper, we address for the first time the issue of quantifying the information asymmetry arising from the network topology. To this aim, we define a novel index - InfoRank - intended to measure the quality of the information possessed by each node, computing the Shannon entropy of the ensemble conditioned on the node-specific information. Further, we test the performance of our novel ranking procedure in terms of the reconstruction accuracy of the (unaccessible) network structure and show that it outperforms other popular centrality measures in identifying the "most informative" nodes. Finally, we discuss the socio-economic implications of network information asymmetry.

cs.SI↗

arXiv2018

'Getting out of the closet': Scientific authorship of literary fiction and knowledge transfer

Some scientists write literary fiction books in their spare time. If these books contain scientific knowledge, literary fiction becomes a mechanism of knowledge transfer. In this case, we could conceptualize literary fiction as non-formal knowledge transfer. We model knowledge transfer via literary fiction as a function of the type of scientist (academic or non-academic) and his/her scientific field. Academic scientists are those employed in academia and public research organizations whereas non-academic scientists are those with a scientific background employed in other sectors. We also distinguish between direct knowledge transfer (the book includes the scientist's research topics), indirect knowledge transfer (scientific authors talk about their research with cultural agents) and reverse knowledge transfer (cultural agents give scientists ideas for future research). Through mixed-methods research and a sample from Spain, we find that scientific authorship accounts for a considerable percentage of all literary fiction authorship. Academic scientists do not transfer knowledge directly so often as non-academic scientists, but the former engage into indirect and reverse transfer knowledge more often than the latter. Scientists from History stand out in direct knowledge transfer. We draw propositions about the role of the academic logic and scientific field on knowledge transfer via literary fiction. We advance some tentative conclusions regarding the consideration of scientific authorship of literary fiction as a valuable knowledge transfer mechanism.

cs.DL↗

arXiv2014

Binary Scientific Star Coauthors Core Size

It is examined whether the relationship $ J \propto A/r^α$, and the subsequent coauthor core notion (Ausloos 2013), between the number ($J$) of joint publications (JP) by a "main scientist" (LI) with her/his coauthors (CAs) can be extended to a team-like system. This is done by considering that each coauthor can be so strongly tied to the LI that they are forming {\it binary scientific star} (BSS) systems with respect to their other collaborators. Moreover, publications in peer review journals and in "proceedings", both often thought to be of "different quality", are separetely distinguished. The role of a time interval for measuring $J$ and $α$ is also examined. New indirect measures are also introduced. For making the point, two LI cases with numerous CAs are studied. It is found that only a few BSS need to be usefully examined. The exponent $α$ turns out to be "second scientist" weakly dependent, but still "size" and "publication type" dependent, according to the number of CAs or JP. The CA core value is found to be (CA or JP) size and publication type dependent, but remains in an understandable range. Somewhat unexpectedly, no special qualitative difference on the binary scientific star CA core value is found between publications in peer review journals and in proceedings. In conclusion, some remark is made on partner cooperation in BSS teams. It is suggested that such measures can serve as criteria for distinguishing the role of scientists in a team.

physics.soc-ph↗