Physics - Physics and Society Publications (50)

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Physics - Physics and Society Publications

We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a linear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Read More


We extend the observability model to multiplex networks. We present mathematical frameworks, valid under the treelike ansatz, able to describe the emergence of the macroscopic cluster of mutually observable nodes in both synthetic and real-world multiplex networks. We show that the observability transition in synthetic multiplex networks is discontinuous. Read More


Inspired by the worldview conceived by Christopher Alexander, a topological representation of cities as a coherent whole has been previously developed to explain why the principles of differentiation and adaptation are essential for sustainable urban design. The design aims to achieve living structures or wholeness that exists to varying degrees in any region of space (see the introductory quote for a brief definition of wholeness). This paper further extends the topological representation or the kind of topological analysis to demonstrate its role in predicting human activities in space. Read More


We present a new inference method based on approximate Bayesian computation for estimating parameters governing an entire network based on link-traced samples of that network. To do this, we first take summary statistics from an observed link-traced network sample, such as a recruitment network of subjects in a hard-to-reach population. Then we assume prior distributions, such as multivariate uniform, for the distribution of some parameters governing the structure of the network and behaviour of its nodes. Read More


The emergence and survival of cooperation is one of the hardest problems still open in science. Several factors such as the existence of punishment, fluctuations in finite systems, repeated interactions and the formation of prestige may all contribute to explain the counter-intuitive prevalence of cooperation in natural and social systems. The characteristics of the interaction networks have been also signaled as an element favoring the persistence of cooperators. Read More


In this paper, we study how the pro-social impact due to the vigilance by other individuals is conditioned by both environmental and evolutionary effects. To this aim, we consider a known model where agents play a Prisoner's Dilemma Game (PDG) among themselves and the pay-off matrix of an individual changes according to the number of neighbors that are "vigilant", i.e. Read More


Fractal scale-free networks are empirically known to exhibit disassortative degree mixing. It is, however, not obvious whether a negative degree correlation between nearest neighbor nodes makes a scale-free network fractal. Here we examine the possibility that disassortativity in complex networks is the origin of fractality. Read More


Population projections are notoriously imprecise and are predicted only with a certain probability. One of reasons is that population growth strongly depends on the total fertility rate (TFR). Globally, TFR declines in long-term since in the near future is expected that will decline below the critical value TFR = 2. Read More


We revisit the Massey's method for rating and ranking in sports and contextualize it as a general centrality measure in network science. Read More


We study how large functional networks can grow stably under possible cascading overload failures and evaluated the maximum stable network size above which even a small-scale failure would cause a fatal breakdown of the network. Employing a model of cascading failures induced by temporally fluctuating loads, the maximum stable size $n_{\text{max}}$ has been calculated as a function of the load reduction parameter $r$ that characterizes how quickly the total load is reduced during the cascade. If we reduce the total load sufficiently fast ($r\ge r_{\text{c}}$), the network can grow infinitely. Read More


Social graph construction from various sources has been of interest to researchers due to its application potential and the broad range of technical challenges involved. The World Wide Web provides a huge amount of continuously updated data and information on a wide range of topics created by a variety of content providers, and makes the study of extracted people networks and their temporal evolution valuable for social as well as computer scientists. In this paper we present SocGraph - an extraction and exploration system for social relations from the content of around 2 billion web pages collected by the Internet Archive over the 17 years time period between 1996 and 2013. Read More


We investigate random search processes on complex networks and for the first time derive an exact expression for the partial cover time that quantifies the time a walker needs to visit multiple targets. Based on that, we find some invariant metrics like the effects of source location and the scale exponent of the size effect, which are independent of the target number. Interestingly, we observe the slow, logarithmic increase of the global partial cover time with the target number across various real networks. Read More


Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. Defining urban boundaries that consider socio-economic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Read More


In this work we review a class of deterministic nonlinear models for the propagation of infectious diseases over contact networks with strongly-connected topologies. We consider network models for susceptible-infected (SI), susceptible-infected-susceptible (SIS), and susceptible-infected-recovered (SIR) settings. In each setting, we provide a comprehensive nonlinear analysis of equilibria, stability properties, convergence, monotonicity, positivity, and threshold conditions. Read More


We extend our variant of the Schelling model incorporating an agent wealth gain function to study the long term evolution of the economic status of neighborhoods in cities. We find that the long term patterns of neighborhood relative economic status (RES) simulated by this model reasonably replicate the empirically observed patterns from American cities. Specifically, we find that larger fractions of rich and poor neighborhoods tend to, on average, retain status for longer than lower- and upper-middle income neighborhoods. Read More


The city is a complex system that evolves through its inherent economic and social interactions. As an important medium facilitating the interaction of people and resources, urban street networks provide an important information source for these patterns. While numerous studies have been conducted on urban infrastructure networks, the specific interplay between street structure and its functional usage, in other words, movement patterns of people and resources, is not yet fully understood (especially in the light of new data sources). Read More


We study the influence of the complex topology of scale-free graphs on the dynamics of anti-coordination games (snowdrift games). These reference models are characterized by the coexistence (evolutionary stable mixed strategy) of two competing species, say "cooperators" and "defectors", and, in finite systems, by metastability and by large-fluctuation-driven fixation. Here, we analyze how the complex scale-free topology affects these properties. Read More


The resilience and fragility of real complex connected systems can be understood through their abrupt behaviors of functioning percolating node clusters against external perturbations based on a random or designed scheme. For the models of the phenomena of the discontinuous transitions against random damages, previous studies focus on the introduction of new dependency or interaction among nodes to generate a more aggressive network breakdown process in the case of networks with undirected interactions or the coupling between layers with different types of interactions to bring about cascading failures in the case of multiplex networks. Yet for many systems whose representation is easily enough as networks with directed interactions, a model of structural resilience with a concisely defined procedure and also an explicit analytical framework is still lacking. Read More


A general formalism is introduced to allow the steady state of non-Markovian processes on networks to be reduced to equivalent Markovian processes on the same substrates. The example of an epidemic spreading process is considered in detail, where all the non-Markovian aspects are shown to be captured within a single parameter, the effective infection rate. Remarkably, this result is independent of the topology of the underlying network, as demonstrated by numerical simulations on two-dimensional lattices and various types of random networks. Read More


Human activities from hunting to emailing are performed in a fractal-like scale invariant pattern. These patterns are considered efficient for hunting or foraging, but are they efficient for gathering information? Here we link the scale invariant pattern of inter-touch intervals on the smartphone to optimal strategies for information gathering. We recorded touchscreen touches in 65 individuals for a month and categorized the activity into checking for information vs. Read More


Online social networks are marketplaces in which memes compete for our attention. While one would expect the best ideas to prevail, empirical evidence suggests that high-quality information has no competitive advantage. Here we investigate this puzzling lack of discriminative power through an agent-based model that incorporates behavioral limitations in managing a heavy flow of information and measures the relationship between the quality of an idea and its likelihood to become prevalent at the system level. Read More


An alternative voting scheme is proposed to fill the democratic gap between a president elected democratically via universal suffrage (deterministic outcome, the actual majority decides), and a president elected by one person randomly selected from the population (probabilistic outcome depending on respective supports). Moving from one voting agent to a group of r randomly selected voting agents reduces the probabilistic character of the outcome. Building r such groups, each one electing its president, to constitute a group of the groups with the r local presidents electing a higher-level president, does reduce further the outcome probabilistic aspect. Read More


Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated to detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated to maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Read More


Agent-based models (ABMs) simulate interactions between autonomous agents in constrained environments over time. ABMs are often used for modeling the spread of infectious diseases. In order to simulate disease outbreaks or other phenomena, ABMs rely on "synthetic ecosystems," or information about agents and their environments that is representative of the real world. Read More


The focus of the current research is to identify people of interest in social networks. We are especially interested in studying dark networks, which represent illegal or covert activity. In such networks, people are unlikely to disclose accurate information when queried. Read More


In the social, behavioral, and economic sciences, it is an important problem to predict which individual opinions will eventually dominate in a large population, if there will be a consensus, and how long it takes a consensus to form. This idea has been studied heavily both in physics and in other disciplines, and the answer depends strongly on both the model for opinions and for the network structure on which the opinions evolve. One model that was created to study consensus formation quantitatively is the Deffuant model, in which the opinion distribution of a population evolves via sequential random pairwise encounters. Read More


We investigate the susceptible-infected-susceptible dynamics on configuration model networks. In an effort for the unification of current approaches, we consider a network whose edges are constantly being rearranged, with a tunable rewiring rate $\omega$. We perform a detailed stationary state analysis of the process, leading to a closed form expression of the absorbing-state threshold for an arbitrary rewiring rate. Read More


We analyze the effect of the social network structure on diffusion of new products in the discrete Bass-SIR model, in which consumers who adopt the product can later "recover" and stop influencing their peers to adopt the product. In the "most-connected" configuration where all consumers are inter-connected (complete network), averaging over all consumers leads to an aggregate model, which combines the Bass model for diffusion of new products with the SIR model for epidemics. In the "least-connected" configuration where consumers are arranged on a circle and each consumer can only be influenced by his left neighbor (one-sided 1D network), averaging over all consumers leads to a different aggregate model which is linear, and can be solved explicitly. Read More


Airbnb, an online marketplace for accommodations, has experienced a staggering growth accompanied by intense debates and scattered regulations around the world. Current discourses, however, are largely focused on opinions rather than empirical evidences. Here, we aim to bridge this gap by presenting the first large-scale measurement study on Airbnb, using a crawled data set containing 2. Read More


Many social networks exhibit some underlying community structure. In particular, in the context of historical research, clustering of different groups into warring or friendly factions can lead to a better understanding of how conflicts may arise, and whether they could be avoided or not. In this work we study the crisis that started in 1225 when the Emperor of the Holy Roman Empire, Frederick II and his son Henry VII got into a conflict which almost led to the rupture and dissolution of the Empire. Read More


Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. Read More


The analysis of networks affects the research of many real phenomena. The complex network structure can be viewed as a network's state at the time of the analysis or as a result of the process through which the network arises. Research activities focus on both and, thanks to them, we know not only many measurable properties of networks but also the essence of some phenomena that occur during the evolution of networks. Read More


We address the problem of long-range memory in the financial markets. There are two conceptually different ways to reproduce power-law decay of auto-correlation function: using fractional Brownian motion as well as non-linear stochastic differential equations. In this contribution we address this problem by analyzing empirical return and trading activity time series from the Forex. Read More


A comprehensive description of ESO in the current global astronomical context, and its plans for the next decade and beyond, are presented. This survey covers all aspects of the Organisation, including the optical-infrared programme at the La Silla Paranal Observatory, the submillimetre facilities ALMA and APEX, the construction of the 39-metre European Extremely Large Telescope and the science operation of these facilities. An extension of the current optical/infrared/submillimetre facilities into multi-messenger astronomy has been made with the decision to host the southern Cherenkov Telescope Array at Paranal. Read More


A modified lattice gas model is proposed to study pedestrian evacuation from a single room. The payoff matrix in this model represents the complicated interactions between selfish individuals, and the mean force imposed on an individual is given by considering the impacts of neighborhood payoff, walls, and defector herding. Each passer-by moves to his selected location according to the Fermi function, and the average velocity of pedestrian flow is defined as a function of the motion rule. Read More


A complex system can be represented and analyzed as a network, where nodes represent the units of the network and edges represent connections between those units. For example, a brain network represents neurons as nodes and axons between neurons as edges. In many networks, some nodes have a disproportionately high number of edges. Read More


This paper aims to establish theoretical foundations of graph product multilayer networks (GPMNs), a family of multilayer networks that can be obtained as a graph product of two or more factor networks. Cartesian, direct (tensor), and strong product operators are considered, and then generalized. We first describe mathematical relationships between GPMNs and their factor networks regarding their degree/strength, adjacency, and Laplacian spectra, and then show that those relationships can still hold for nonsimple and generalized GPMNs. Read More


Novelties are part of our daily lives. We constantly adopt new technologies, conceive new ideas, meet new people, experiment with new situations. Occasionally, we as individuals, in a complicated cognitive and sometimes fortuitous process, come up with something that is not only new to us, but to our entire society so that what is a personal novelty can turn into an innovation at a global level. Read More


Critical infrastructures form a technological skeleton of our world by providing us with water, food, electricity, gas, transportation, communication, banking, and finance. Moreover, as urban population increases, the role of infrastructures become more vital. In this paper, we adopt a network perspective and discuss the ever growing need for fundamental interdisciplinary study of critical infrastructure networks, efficient methods for estimating their reliability, and cost-effective strategies for enhancing their resiliency. Read More


A characteristic property of networks is their ability to propagate influences, such as infectious diseases, behavioral changes, and failures. An especially important class of such contagious dynamics is that of cascading processes. These processes include, for example, cascading failures in infrastructure systems, extinctions cascades in ecological networks, and information cascades in social systems. Read More


Graph theoretical analysis of the community structure of networks attempts to identify the communities (or modules) to which each node affiliates. However, this is in most cases an ill-posed problem, as the affiliation of a node to a single community is often ambiguous. Previous solutions have attempted to identify all of the communities to which each node affiliates. Read More


We analyze the fine-grained connections between the average degree and the power-law degree distribution exponent in growing information networks. Our starting observation is a power-law degree distribution with a decreasing exponent and increasing average degree as a function of the network size. Our experiments are based on three Twitter at-mention networks and three more from the Koblenz Network Collection. Read More


Using the generalized entropies which depend on two parameters we propose a set of quantitative characteristics derived from the Information Geometry based on these entropies. Our aim, at this stage, is modest, as we are first constructing some fundamental geometric objects. We first establish the existence of a two-parameter family of probability distributions. Read More


In many social systems, groups of individuals can find remarkably efficient solutions to complex cognitive problems, sometimes even outperforming a single expert. The success of the group, however, crucially depends on how the judgments of the group members are aggregated to produce the collective answer. A large variety of such aggregation methods have been described in the literature, such as averaging the independent judgments, relying on the majority or setting up a group discussion. Read More


We investigate the relationship between social structure and sentiment through the analysis of half a million tweets about the Irish Marriage Referendum of 2015. We obtain the sentiment of every tweet with the hashtags #marref and #marriageref posted in the days leading to the referendum, and construct networks to aggregate sentiment and study the interactions among users. The sentiment of the mention tweets that a user sends is correlated with the sentiment of the mentions received, and there are significantly more connections between users with similar sentiment scores than among users with opposite scores. Read More


It has been shown in recent years that the stochastic block model is undetectable in the sparse limit, i.e., that no algorithm can identify a partition correlated with the partition used to generate an instance, if the instance is sparse and infinitely large. Read More


Interorganizational interactions are often complex and paradoxical. In this research, we transcend two management paradoxes: competition versus cooperation and open-source versus proprietary technology development. We follow the OpenStack open-source ecosystem where competing firms cooperate in the joint-development of a cloud infrastructure for big data. Read More


We present a sampling of analyses concerning the gender ratio of plenary speakers during the years 2000--2016 and make comparisons with other conferences, such as the APS April meeting. We hope this will invite discussion of ideas for how to make our field more accessible to women and minorities. We are preparing for an in-depth survey of the lattice field and welcome any ideas or suggestions. Read More


Advances in experimental techniques are generating an increasing volume of publicly available ecologically and biologically relevant data and are revealing that living systems are characterized by the emergence of recurrent patterns and regularities. Several studies indicate that metabolic, gene-regulatory and species interaction networks possess a non-random architecture. One of the observed emergent patterns is sparsity, i. Read More


We study a subclass of the May-Leonard stochastic model with an arbitrary even number of species, leading to the arising of two competing partnerships where individuals are indistinguishable. By carrying out a series of accurate numerical stochastic simulations, we show that alliances compete each other forming spatial domains bounded by interfaces of empty sites. We solve numerically the mean field equations associated to the stochastic model in one and two spatial dimensions. Read More