Physics - Physics and Society Publications (50)

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Physics - Physics and Society Publications

Pedestrian crowds often include social groups, i.e. pedestrians that walk together because of social relationships. Read More


The syntactic structure of a sentence can be modelled as a tree, where vertices correspond to words and edges indicate syntactic dependencies. It has been claimed recurrently that the number of edge crossings in real sentences is small. However, a baseline or null hypothesis has been lacking. Read More


Estimating distributions of labels associated with nodes (e.g., number of connections or citizenship of users in a social network) in large graphs via sampling is a vital part of the study of complex networks. Read More


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. Read More


Message-passing methods provide a powerful approach for calculating the expected size of cascades either on random networks (e.g., drawn from a configuration-model ensemble or its generalizations) asymptotically as the number $N$ of nodes becomes infinite or on specific finite-size networks. Read More


By modeling macro-economical indicators using digital traces of human activities on mobile or social networks, we can provide important insights to processes previously assessed via paper-based surveys or polls only. We collected aggregated workday activity timelines of US counties from the normalized number of messages sent in each hour on the online social network Twitter. In this paper, we show how county employment and unemployment statistics are encoded in the daily rhythm of people by decomposing the activity timelines into a linear combination of two dominant patterns. Read More


A major challenge in network science is to determine whether an observed network property reveals some non-trivial behavior of the network's nodes, or if it is a consequence of the network's elementary properties. Statistical null models serve this purpose by producing random networks whilst keeping chosen network's properties fixed. While there is increasing interest in networks that evolve in time, we still lack a robust time-aware framework to assess the statistical significance of the observed structural properties of growing networks. Read More


Centrality is an important notion in complex networks; it could be used to characterize how influential a node or an edge is in the network. It plays an important role in several other network analysis tools including community detection. Even though there are a small number of axiomatic frameworks associated with this notion, the existing formalizations are not generic in nature. Read More


We demonstrate the application of the multiplex networks-approach for the analysis of various networks which connected individuals and communities in the politically highly fragmented late medieval Balkans (1204-1453 AD) within and across border zones. We present how we obtain relational data from our sources and the integration of these data into three different networks (of roads, state administration and ecclesiastical administration) of various topologies; then we calculate several indicators for influences and overlaps between these different networks which connect the same set of nodes (settlements). We analyse changes and continuities in the topologies of the various networks for three time-steps (1210, 1324 and 1380 CE) and demonstrate the role of these networks as frameworks for social interactions. Read More


Human decision making underlies data generating process in multiple application areas, and models explaining and predicting choices made by individuals are in high demand. Discrete choice models are widely studied in economics and computational social sciences. As digital social networking facilitates information flow and spread of influence between individuals, new advances in modeling are needed to incorporate social information into these models in addition to characteristic features affecting individual choices. Read More


We study the critical behavior of a continuous opinion model, driven by kinetic exchanges in a fully-connected population. Opinions range in the real interval $[-1,1]$, representing the different shades of opinions against and for an issue under debate. Individual's opinions evolve through pairwise interactions, with couplings that are typically positive, but a fraction $p$ of negative ones is allowed. Read More


The recent growth in interest in the physics and mathematics of networks has been driven in large part by the increasing availability of data describing the structure of networks ranging from the internet and the web to social and biological networks. It is a surprising feature of many empirical studies, however, that the data are reported without any estimate of their expected accuracy, even though it is clear that most do suffer from measurement error of various kinds. In this paper we develop the theory of measurement error for network data and give an expectation-maximization algorithm for estimating both false positive and false negative rates for edges in observed networks. Read More


The most critical time for information to spread is in the aftermath of a serious emergency, crisis, or disaster. Individuals affected by such situations can now turn to an array of communication channels, from mobile phone calls and text messages to social media posts, when alerting social ties. These channels drastically improve the speed of information in a time-sensitive event, and provide extant records of human dynamics during and afterward the event. Read More


In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Read More


Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and their shared environment. As a result, predicting when, where, and how far diseases will spread requires a complex systems approach to modeling. Recent studies have demonstrated that predicting different components of outbreaks--e. Read More


This paper offers the first systematic presentation of the topological approach to the analysis of epidemic and pseudo-epidemic spatial processes. We introduce the basic concepts and proofs, at test the approach on a diverse collection of case studies of historically documented epidemic and pseudo-epidemic processes. The approach is found to consistently provide reliable estimates of the structural features of epidemic processes, and to provide useful analytical insights and interpretations of fragmentary pseudo-epidemic processes. Read More


Many problems in industry --- and in the social, natural, information, and medical sciences --- involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this article, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry. Read More


The spider silk is one of the most interesting bio-materials investigated in the last years. One of the main reasons that brought scientists to study this organized system is its high level of resistance if compared to other artificial materials characterized by higher density. Subsequently, researchers discovered that the spider silk is a complex system formed by different kinds of proteins, organized (or disorganized) to guarantee the required resistance, which is function of the final application and of the environmental conditions. Read More


Promoting information spreading is a booming research topic in network science community. However, the exiting studies about promoting information spreading seldom took into account the human memory, which plays an important role in the spreading dynamics. In this paper we propose a non-Markovian information spreading model on complex networks, in which every informed node contacts a neighbor by using the memory of neighbor's accumulated contact numbers in the past. Read More


The friendship paradox states that in a social network, egos tend to have lower degree than their alters, or, "your friends have more friends than you do". Most research has focused on the friendship paradox and its implications for information transmission, but treating the network as static and unweighted. Yet, people can dedicate only a finite fraction of their attention budget to each social interaction: a high-degree individual may have less time to dedicate to individual social links, forcing them to modulate the quantities of contact made to their different social ties. Read More


We consider the estimation of binary election outcomes as martingales and propose an arbitrage pricing when one continuously updates estimates. We argue that the estimator needs to be priced as a binary option as the arbitrage valuation minimizes the conventionally used Brier score for tracking the accuracy of probability assessors. We create a dual martingale process $Y$, in $[L,H]$ from the standard arithmetic Brownian motion, $X$ in $(-\infty, \infty)$ and price elections accordingly. Read More


Structural discrimination appears to be a persistent phenomenon in social systems. We here outline the hypothesis that it can result from the evolutionary dynamics of the social system itself. We study the evolutionary dynamics of agents with neutral badges in a simple social game and find that the badges are readily discriminated by the system although not being tied to the payoff matrix of the game. Read More


Astrobiology is usually defined as the study of the origin, evolution, distribution, and future of life in the universe. As such it is inherently interdisciplinary and cannot help but engender a worldview infused by cosmic and evolutionary perspectives. Both these attributes of the study of astrobiology are, and will increasingly prove to be, beneficial to society regardless of whether extraterrestrial life is discovered or not. Read More


Relations among species in ecosystems can be represented by complex networks where both negative (competition) and positive (mutualism) interactions are concurrently present. Recently, it has been shown that many ecosystems can be cast into mutualistic networks, and that nestedness reduces effective inter-species competition, thus facilitating mutually beneficial interactions and increasing the number of coexisting species or the biodiversity. However, current approaches neglect the structure of inter-species competition by adopting a mean-field perspective that does not deal with competitive interactions properly. Read More


We explore how to study dynamical interactions between brain regions using functional multilayer networks whose layers represent the different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as a multilayer network in which all brain regions can interact with each other at different frequency bands, instead of as a multiplex network, in which interactions between different frequency bands are only allowed within each brain region and not between them. We study the second smallest eigenvalue of the combinatorial supra-Laplacian matrix of the multilayer network in detail, and we thereby show that the heterogeneity of interlayer edges and, especially, the fraction of missing edges crucially modify the spectral properties of the multilayer network. Read More


Multilayer structures can induce profound changes in the dynamics of systems running on networks. Features as link overlap or degree correlation across layers, for example, contribute to reinforce certain interactions and play important roles in dynamical processes. In this work, we investigate how a multilayer structure affects the dynamics of an ageing voter model. Read More


It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using `social bots' deployed to carry out coordinated attempts at spreading information. Read More


We explore how the polarization around controversial topics evolves on Twitter - over a long period of time (2011 to 2016), and also as a response to major external events that lead to increased related activity. We find that increased activity is typically associated with increased polarization; however, we find no consistent long-term trend in polarization over time among the topics we study. Read More


Population control policies are proposed and in some places employed as a means towards curbing population growth. This paper is concerned with a disturbing side-effect of such policies, namely, the potential risk of societal fragmentation due to changes in the distribution of family sizes. This effect is illustrated in some simple settings and demonstrated by simulation. Read More


Haynes et al. (1977) derived a nonlinear differential equation to determine the spread of innovations within a social network across space and time. This model depends upon the imitators and the innovators within the social system, where the imitators respond to internal influences, whilst the innovators react to external factors. Read More


The search engine is tightly coupled with social networks and is primarily designed for users to acquire interested information. Specifically, the search engine assists the information dissemination for social networks, i.e. Read More


We formulate and propose an algorithm (MultiRank) for the ranking of nodes and layers in large multiplex networks. MultiRank takes into account the full multiplex network structure of the data and exploits the dual nature of the network in terms of nodes and layers. The proposed centrality of the layers (influences) and the centrality of the nodes are determined by a coupled set of equations. Read More


We demonstrate that behavioral probabilities of human decision makers share many common features with quantum probabilities. This does not imply that humans are some quantum objects, but just shows that the mathematics of quantum theory is applicable to the description of human decision making. The applicability of quantum rules for describing decision making is connected with the nontrivial process of making decisions in the case of composite prospects under uncertainty. Read More


Finding influential spreaders of information and disease in networks is an important theoretical problem, and one of considerable recent interest. It has been almost exclusively formulated as a node-ranking problem -- methods for identifying influential spreaders rank nodes according to how influential they are. In this work, we show that the ranking approach does not necessarily work: the set of most influential nodes depends on the number of nodes in the set. Read More


Modern social media platforms facilitate the rapid spread of information online. Modelling phenomena such as social contagion and information diffusion are contingent upon a detailed understanding of the information-sharing processes. In Twitter, an important aspect of this occurs with retweets, where users rebroadcast the tweets of other users. Read More


We reveal the role of fluctuations in percolation of sparse complex networks. To this end we consider two random realizations of the initial damage of the nodes and we evaluate the fraction of nodes that are expected to remain in the giant component of the network in both cases or just in one case. Our framework includes a message-passing algorithm able to predict the fluctuations in a single network, and an analytic prediction of the expected fluctuations in ensembles of sparse networks. Read More


Multiplex networks offer an important tool for the study of complex systems and extending techniques originally designed for single--layer networks is an important area of study. One of the most important methods for analyzing networks is clustering the nodes into communities that represent common connectivity patterns. In this paper we extend spectral clustering to multiplex structures and discuss some of the difficulties that arise in attempting to define a natural generalization. Read More


Locating sources of diffusion and spreading from minimum data is a significant problem in network science with great applied values to the society. However, a general theoretical framework dealing with optimal source localization is lacking. Combining the controllability theory for complex networks and compressive sensing, we develop a framework with high efficiency and robustness for optimal source localization in arbitrary weighted networks with arbitrary distribution of sources. Read More


This paper analyses the DeGroot-Friedkin model for evolution of the individuals' social powers in a social network when the network topology varies dynamically (described by dynamic relative interaction matrices). The DeGroot-Friedkin model describes how individual social power (self-appraisal, self-weight) evolves as a network of individuals discuss a sequence of issues. We seek to study dynamically changing relative interactions because interactions may change depending on the issue being discussed. Read More


This paper analyzes the world web of mergers and acquisitions (M&As) using a complex network approach. We use data of M&As to build a temporal sequence of binary and weighted-directed networks, for the period 1995-2010 and 224 countries. We study different geographical and temporal aspects of the international M&As network (IMAN), building sequences of filtered sub-networks whose links belong to specific intervals of distance or time. Read More


Ranges of differentiated abstention are shown to reverse an "exact" poll estimate on voting day allowing the minority candidate to win the election. In a two-candidate competition A and B with voting intentions at $I_a$, $I_b=1-I_a$ and respective turnout at $x$ and $y$, there exists a critical value $I_{ac}$ for which $I_{ac}\frac{1}{2}$. The reversal may occur without any change of individual choices. Read More


We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Read More


Devising strategies for economic development in a globally competitive landscape requires a solid and unbiased understanding of countries' technological advancement and similarities among export products. Both can be addressed through the bipartite representation of the International Trade Network. In the present paper, we apply the recently proposed grand canonical projection algorithm to uncover country and product communities. Read More


Demonstrations, protests, riots, and shifts in public opinion respond to the coordinating potential of communication networks. Digital technologies have turned interpersonal networks into massive, pervasive structures that constantly pulsate with information. Here, we propose a model that aims to analyze the contagion dynamics that emerge in networks when repeated activation is allowed, that is, when actors can engage recurrently in a collective effort. Read More


Although various norms for reciprocity-based cooperation have been suggested that are evolutionarily stable against invasion from free riders, the process of alternation of norms and the role of diversified norms remain unclear in the evolution of cooperation. We clarify the co-evolutionary dynamics of norms and cooperation in indirect reciprocity and also identify the indispensable norms for the evolution of cooperation. Inspired by the gene knockout method, a genetic engineering technique, we developed the norm knockout method and clarified the norms necessary for the establishment of cooperation. Read More


Nowadays online searches are undeniably the most common form of information gathering, as witnessed by billions of clicks generated each day on search engines. In this work we describe online searches as foraging processes that take place on the semi-infinite line. Using a variety of quantities like probability distributions and complementary cumulative distribution functions of step-length and waiting time as well as mean square displacements and entropies, we analyze three different click-through logs that contain the detailed information of millions of queries submitted to search engines. Read More


Many real-world systems are characterized by stochastic dynamical rules where a complex network of dependencies among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules of the dynamical process, the ability to predict system configurations is generally characterized by large uncertainty. Sampling a fraction of the nodes and deterministically observing their state may help to reduce the uncertainty about the unobserved nodes. Read More


Most people simultaneously belong to several distinct social networks, in which their relations can be different. They have opinions about certain topics, which they share and spread on these networks, and are influenced by the opinions of other persons. In this paper, we build upon this observation to propose a new nodal centrality measure for multiplex networks. Read More


What characteristics distinguish liars from truth-tellers? Recent research has explored "when" and "why" people lie. Yet little is known about "who" lies. Previous studies have led to mixed results about the effect of gender on deception, and they have largely neglected the role of other characteristics, such as age and level of education. Read More


Complex networks are graph representation of complex systems from the real-world. They are ubiquitous in biological, ecological, social and infrastructural systems. Here we study a transformation of these complex networks into simplicial complexes, where cliques represent the simplicies of the complex. Read More