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At least 739 records · Page 41Linked to original sources
arXiv2013

The simplicity of planar networks

Shortest paths are not always simple. In planar networks, they can be very different from those with the smallest number of turns - the simplest paths. The statistical comparison of the lengths of the shortest and simplest paths provides a non trivial and non local information about the spatial organization of these graphs. We define the simplicity index as the average ratio of these lengths and the simplicity profile characterizes the simplicity at different scales. We measure these metrics on artificial (roads, highways, railways) and natural networks (leaves, slime mould, insect wings) and show that there are fundamental differences in the organization of urban and biological systems, related to their function, navigation or distribution: straight lines are organized hierarchically in biological cases, and have random lengths and locations in urban systems. In the case of time evolving networks, the simplicity is able to reveal important structural changes during their evolution.

physics.soc-ph↗
arXiv2013

Environment-dependent payoffs in finite populations

In constant-payoff finite population games, when selection is weak and population size is large, the one-third law serves as the condition for a strategy to be advantageous. We generalize the result to the case where payoff matrices are environment-dependent and provide a more general law. In this way we model feedback from the environment and show its impact on the dynamics.

q-bio.PE↗
arXiv2013

Benefits to the U.S. from Physicists Working at Accelerators Overseas

We illustrate benefits to the U.S. economy and technological infrastructure of U.S. participation in accelerators overseas. We discuss contributions to experimental hardware and analysis and to accelerator technology and components, and benefits stemming from the involvement of U.S. students and postdoctoral fellows in global scientific collaborations. Contributed to the proceedings of the Snowmass 2013 Community Summer Study.

physics.soc-ph↗
arXiv2013

Urban Landscape is an Important Factor in Rising Inequality, Spatial Segregation, and Social Isolation

Urbanization has been the dominant demographic trend in the entire world, during the last half century. Rural to urban migration, international migration, and the re-classification or expansion of existing city boundaries have been among the major reasons for increasing urban population. The essentially fast growth of cities in the last decades urgently calls for a profound insight into the common principles stirring the structure of urban developments all over the world. We have discussed the graph representations of urban spatial structures and suggested a computationally simple technique that can be used in order to spot the relatively isolated locations and neighborhoods, to detect urban sprawl, and to illuminate the hidden community structures in complex urban textures. The approach may be implemented for the detailed expertise of any urban pattern and the associated transport networks that may include many transportation modes.

physics.soc-ph↗
arXiv2013

Driving forces in researchers mobility

Starting from the dataset of the publication corpus of the APS during the period 1955-2009, we reconstruct the individual researchers trajectories, namely the list of the consecutive affiliations for each scholar. Crossing this information with different geographic datasets we embed these trajectories in a spatial framework. Using methods from network theory and complex systems analysis we characterise these patterns in terms of topological network properties and we analyse the dependence of an academic path across different dimensions: the distance between two subsequent positions, the relative importance of the institutions (in terms of number of publications) and some socio-cultural traits. We show that distance is not always a good predictor for the next affiliation while other factors like "the previous steps" of the career of the researchers (in particular the first position) or the linguistic and historical similarity between two countries can have an important impact. Finally we show that the dataset exhibit a memory effect, hence the fate of a career strongly depends from the first two affiliations.

physics.soc-ph↗
arXiv2013

Shadow networks: Discovering hidden nodes with models of information flow

Complex, dynamic networks underlie many systems, and understanding these networks is the concern of a great span of important scientific and engineering problems. Quantitative description is crucial for this understanding yet, due to a range of measurement problems, many real network datasets are incomplete. Here we explore how accidentally missing or deliberately hidden nodes may be detected in networks by the effect of their absence on predictions of the speed with which information flows through the network. We use Symbolic Regression (SR) to learn models relating information flow to network topology. These models show localized, systematic, and non-random discrepancies when applied to test networks with intentionally masked nodes, demonstrating the ability to detect the presence of missing nodes and where in the network those nodes are likely to reside.

physics.soc-ph↗
arXiv2013

Data mining the EXFOR database using network theory

The EXFOR database contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We created an undirected graph from the EXFOR datasets with graph nodes representing single observables and graph links representing the various types of connections between these observables. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. By analyzing this abstract graph, we are able to address very specific questions such as 1) what observables are being used as reference measurements by the experimental nuclear science community? 2) are these observables given the attention needed by various nuclear data evaluation projects? 3) are there classes of observables that are not connected to these reference measurements? In addressing these questions, we propose several (mostly cross section) observables that should be evaluated and made into reaction reference standards.

nucl-th↗
arXiv2013

Resource Redistribution under Lack of Information: Short-Term Recovery after Large Scale Disasters

The paper is devoted to the problem of disaster mitigation. It develops an emergent mechanism of resource redistribution aimed at recovering of a socio-technological system affected by a large scale disaster. The basic requirements to the short-term recovery are taken into account in constructing this mechanism. The system at hand consists of many individual units (cities) and the mechanism is based on their cooperative interaction, which makes the resource redistribution efficient regardless of the particular position of affected region. Previously we studied the dynamics of supply process governed by this mechanism when all the information about the system is available and actual from the beginning of the process (V. Lubashevskiy, T. Kanno, K. Furuta, arXiv:1310.0648). In the present paper we analyze the effects of lack of information on the resource delivery rate. Two scenarios of the lack of information are allowed for. The first one is the delay of the information about the city states. The second one is its incompleteness. As a result of simulation, it is demonstrated that the duration of the resource redistribution governed by the developed mechanism is weakly affected by the lack of information.

nlin.AO↗
arXiv2013

Topic and Sentiment Analysis on OSNs: a Case Study of Advertising Strategies on Twitter

Social media have substantially altered the way brands and businesses advertise: Online Social Networks provide brands with more versatile and dynamic channels for advertisement than traditional media (e.g., TV and radio). Levels of engagement in such media are usually measured in terms of content adoption (e.g., likes and retweets) and sentiment, around a given topic. However, sentiment analysis and topic identification are both non-trivial tasks. In this paper, using data collected from Twitter as a case study, we analyze how engagement and sentiment in promoted content spread over a 10-day period. We find that promoted tweets lead to higher positive sentiment than promoted trends; although promoted trends pay off in response volume. We observe that levels of engagement for the brand and promoted content are highest on the first day of the campaign, and fall considerably thereafter. However, we show that these insights depend on the use of robust machine learning and natural language processing techniques to gather focused, relevant datasets, and to accurately gauge sentiment, rather than relying on the simple keyword- or frequency-based metrics sometimes used in social media research.

cs.SI↗
arXiv2013

The expert game -- Cooperation in social communication

Large parts of professional human communication proceed in a request-reply fashion, whereby requests contain specifics of the information desired while replies can deliver the required information. However, time limitations often force individuals to prioritize some while neglecting others. This dilemma will inevitably force individuals into defecting against some communication partners to give attention to others. Furthermore, communication entirely breaks down when individuals act purely egoistically as replies would never be issued and quest for desired information would always be prioritized. Here we present an experiment, termed "The expert game", where a number of individuals communicate with one-another through an electronic messaging system. By imposing a strict limit on the number of sent messages, individuals were required to decide between requesting information that is beneficial for themselves or helping others by replying to their requests. In the experiment, individuals were assigned the task to find the expert on a specific topic and receive a reply from that expert. Tasks and expertise of each player were periodically re-assigned to randomize the required interactions. Resisting this randomization, a non-random network of cooperative communication between individuals formed. We use a simple Bayesian inference algorithm to model each player's trust in the cooperativity of others with good experimental agreement. Our results suggest that human communication in groups of individuals is strategic and favors cooperation with trusted parties at the cost of defection against others. To establish and maintain trusted links a significant fraction of time-resources is allocated, even in situations where the information transmitted is negligible.

cs.SI↗
arXiv2013

Multi-dimensional Conversation Analysis across Online Social Networks

With the advance of the Internet, ordinary users have created multiple personal accounts on online social networks, and interactions among these social network users have recently been tagged with location information. In this work, we observe user interactions across two popular online social networks, Facebook and Twitter, and analyze which factors lead to retweet/like interactions for tweets/posts. In addition to the named entities, lexical errors and expressed sentiments in these data items, we also consider the impact of shared user locations on user interactions. In particular, we show that geolocations of users can greatly affect which social network post/tweet will be liked/ retweeted. We believe that the results of our analysis can help researchers to understand which social network content will have better visibility.

cs.SI↗
arXiv2013

Empirical modeling of the impact factor distribution

The distribution of impact factors has been modeled in the recent informetric literature using two-exponent law proposed by Mansilla et al. (2007). This paper shows that two distributions widely-used in economics, namely the Dagum and Singh-Maddala models, possess several advantages over the two-exponent model. Compared to the latter, the former give as good as or slightly better fit to data on impact factors in eight important scientific fields. In contrast to the two-exponent model, both proposed distributions have closed-from probability density functions and cumulative distribution functions, which facilitates fitting these distributions to data and deriving their statistical properties.

cs.DL↗
arXiv2013

Spatially embedded growing small-world networks

Networks in nature are often formed within a spatial domain in a dynamical manner, gaining links and nodes as they develop over time. We propose a class of spatially-based growing network models and investigate the relationship between the resulting statistical network properties and the dimension and topology of the space in which the networks are embedded. In particular, we consider models in which nodes are placed one by one in random locations in space, with each such placement followed by configuration relaxation toward uniform node density, and connection of the new node with spatially nearby nodes. We find that such growth processes naturally result in networks with small-world features, including a short characteristic path length and nonzero clustering. These properties do not appear to depend strongly on the topology of the embedding space, but do depend strongly on its dimension; higher-dimensional spaces result in shorter path lengths but less clustering.

physics.soc-ph↗
arXiv2013

Maximum Coverage and Maximum Connected Covering in Social Networks with Partial Topology Information

Viral marketing campaigns seek to recruit the most influential individuals to cover the largest target audience. This can be modeled as the well-studied maximum coverage problem. There is a related problem when the recruited nodes are connected. It is called the maximum connected cover problem. This problem ensures a strong coordination between the influential nodes which are the backbone of the marketing campaign. In this work, we are interested on both of these problems. Most of the related literature assumes knowledge about the topology of the network. Even in that case, the problem is known to be NP-hard. In this work, we propose heuristics to the maximum connected cover problem and the maximum coverage problem with different knowledge levels about the topology of the network. We quantify the difference between these heuristics and the local and global greedy algorithms.

cs.SI↗
arXiv2013

A network analysis of Sibiu County, Romania

Network science methods have proved to be able to provide useful insights from both a theoretical and a practical point of view in that they can better inform governance policies in complex dynamic environments. The tourism research community has provided an increasing number of works that analyse destinations from a network science perspective. However, most of the studies refer to relatively small samples of actors and linkages. With this note we provide a full network study, although at a preliminary stage, that reports a complete analysis of a Romanian destination (Sibiu). Our intention is to increase the set of similar studies with the aim of supporting the investigations in structural and dynamical characteristics of tourism destinations.

physics.soc-ph↗
arXiv2013

Phase transition in the controllability of temporal networks

The control of complex systems is an ongoing challenge of complexity research. Recent advances using concepts of structural control deduce a wide range of control related properties from the network representation of complex systems. Here, we examine the controllability of complex systems for which the timescale of the dynamics we control and the timescale of changes in the network are comparable. We provide both analytical and computational tools to study controllability based on temporal network characteristics. We apply these results to investigate the controllable subnetwork using a single input, present analytical results for a generic class of temporal network models, and preform measurements using data collected from a real system. Depending upon the density of the interactions compared to the timescale of the dynamics, we witness a phase transition describing the sudden emergence of a giant controllable subspace spanning a finite fraction of the network. We also study the role of temporal patterns and network topology in real data making use of various randomization procedures, finding that the overall activity and the degree distribution of the underlying network are the main features influencing controllability.

cond-mat.stat-mech↗
arXiv2013

Interactive Sensing in Social Networks

This paper presents models and algorithms for interactive sensing in social networks where individuals act as sensors and the information exchange between individuals is exploited to optimize sensing. Social learning is used to model the interaction between individuals that aim to estimate an underlying state of nature. In this context the following questions are addressed: How can self-interested agents that interact via social learning achieve a tradeoff between individual privacy and reputation of the social group? How can protocols be designed to prevent data incest in online reputation blogs where individuals make recommendations? How can sensing by individuals that interact with each other be used by a global decision maker to detect changes in the underlying state of nature? When individual agents possess limited sensing, computation and communication capabilities, can a network of agents achieve sophisticated global behavior? Social and game theoretic learning are natural settings for addressing these questions. This article presents an overview, insights and discussion of social learning models in the context of data incest propagation, change detection and coordination of decision making.

cs.SI↗