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arXiv2014

Which factor dominates the industry evolution? A synergy analysis based on China's ICT industry

Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on industry convergence and technology progress. Firstly, we use self-organization method and Haken Model to establish synergetic evolution equations, select technology progress and industry convergence as the key variables of industry evolution system; then use patent licensing data of china's listed ICT companies to measure industry convergence rate and apply DEA Malmquist index method to calculate technology progress level; furthermore apply simultaneous equation estimation method to investigate the synergetic industry evolution process. From 2002 to 2012, China's ICT industry develops rapidly; it has the most obvious convergence and powerful technology progress compared with other industries. We choose china's listed ICT industry to make empirical analysis. Our main findings are: a) technology progress is the order parameter which dominates industry system evolution. Moreover, industry convergence is the control parameter which is influenced by technology progress; b) Development of technology progress is the core factor for causing evolution of industry system, and industry convergence is the outcome of technology progress; c) Especially, it is important that the dominated role of technology progress will be sustained, even though in the environment of convergence, companies also need focus on self-innovation, rather than only adapt to the new industry evolution trend.

physics.soc-ph↗

arXiv2014

DecLiNe -- Models for Decay of Links in Networks

The prediction of graph evolution is an important and challenging problem in the analysis of networks and of the Web in particular. But while the appearance of new links is part of virtually every model of Web growth, the disappearance of links has received much less attention in the literature. To fill this gap, our approach DecLiNe (an acronym for DECay of LInks in NEtworks) aims to predict link decay in networks, based on structural analysis of corresponding graph models. In analogy to the link prediction problem, we show that analysis of graph structures can help to identify indicators for superfluous links under consideration of common network models. In doing so, we introduce novel metrics that denote the likelihood of certain links in social graphs to remain in the network, and combine them with state-of-the-art machine learning methods for predicting link decay. Our methods are independent of the underlying network type, and can be applied to such diverse networks as the Web, social networks and any other structure representable as a network, and can be easily combined with case-specific content analysis and adopted for a variety of social network mining, filtering and recommendation applications. In systematic evaluations with large-scale datasets of Wikipedia we show the practical feasibility of the proposed structure-based link decay prediction algorithms.

cs.SI↗

arXiv2014

Social insect colony as a biological regulatory system: Information flow in dominance networks

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp \textit{Ropalidia marginata} - a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of \textit{R. marginata} are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure - the `feed-forward loop' - a key functional component in many other information transfer networks. The dynamical analysis through Boolean modeling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.

q-bio.PE↗

arXiv2014

Kinetic modeling of opinion formation of peoples via multiple political parties

We investigate the opinion formation among the peoples and multiple political parties using the one dimensional relativistic Boltzmann-Vlasov equation for multi-components. A political party is constituted of politicians. The opinion formation depends on self-thinkings of peoples and politicians, and the constraint of the political party over opinions of politicians, when we restrict ourselves to the conciliatory exchange of opinions between two individuals. In particular, shock like profiles are obtained in the distribution of opinions of peoples, when the self-thinking of politicians are absent at the binary exchange of opinions between two politicians in the same political party.

physics.soc-ph↗

arXiv2014

From Behavioral Psychology to Acceleration Modeling: Calibration, Validation, and Exploration of Drivers Cognitive and Safety Parameters in a Risk-Taking Environment

We investigate a utility-based approach for driver car-following behavioral modeling while analyzing different aspects of the model characteristics especially in terms of capturing different fundamental diagram regions and safety proxy indices. The adopted model came from an elementary thought where drivers associate subjective utilities for accelerations (i.e. gain in travel times) and subjective dis-utilities for decelerations (i.e. loss in travel time) with a perceived probability of being involved in rear-end collision crashes. Following the testing of the model general structure, the authors translate the corresponding behavioral psychology theory - prospect theory - into an efficientmicroscopic traffic modeling with more elaborate stochastic characteristics considered in a risk-taking environment. The formulated model offers a better understanding of drivers behavior, particularly under extreme/incident conditions.

physics.soc-ph↗

arXiv2014

Organised crime infiltration in the legitimate private economy - An empirical network analysis approach

It is estimated that Italian Mafias registered 135 billion euros in profits only in 2010. Part of this huge amount of money, coming mostly from the drugs, prostitution and arms illicit markets, is often used to invest into legitimate private economies. As a consequence, the affected economies destabilise, become entrenched with violent forms of competition and are bound to stagnation. Nonetheless, few are the attempts to uncover the patterns followed by criminal organisations in their business ventures. The reason lays mostly in the poor availability of data on criminal activity, or in the highly risky task of gather it. This paper partially fills this gap thanks to access to information about the Sicilian Mafia in a city. More specifically, it tries to analyse the nature and extent of criminal infiltration into the legitimate private economy of the case-study using network techniques. The research demonstrates that sectors with a high degree of centrality and comprising fewer firms are the most vulnerable to this kind of security threat. It also shows that centrality is also the key criterion that makes a firm sensitive to infiltration, provided it belongs to a susceptible economic sector.

cs.CY↗

arXiv2014

General Centrality in a hypergraph

The goal of this paper is to present a centrality measurement for the nodes of a hypergraph, by using existing literature which extends eigenvector centrality from a graph to a hypergraph, and literature which give a general centrality measurement for a graph. We will use this measurement to say more about the number of communications in a hypergraph, to implement a learning mechanism, and to construct certain networks.

cs.SI↗

arXiv2014

Modeling Collaborations with Persistent Homology

In this paper we describe a model based on persistent homology that describes interactions between mathematicians in terms of collaborations. Some ideas from classical data analysis are used.

math.AT↗

arXiv2014

Link aggregation process for modelling weighted mutualistic networks

Mutualism is a biological interaction mutually beneficial for both species involved, such as the interaction between plants and their pollinators. Real mutualistic communities can be understood as weighted bipartite networks and they present a nested structure and truncated power law degree and strength distributions. We present a novel link aggregation model that works on a strength-preferential attachment rule based on the Individual Neutrality hypothesis. The model generates mutualistic networks with emergent nestedness and truncated distributions. We provide some analytical results and compare the simulated and empirical network topology. Upon further improving the shape of the distributions, we have also studied the role of forbidden interactions on the model and found that the inclusion of forbidden links does not prevent for the appearance of super-generalist species. A Python script with the model algorithms is available.

q-bio.PE↗

arXiv2014

Heterogeneous epidemic model for assessing data dissemination in opportunistic networks

In this paper we investigate a susceptible-infected-susceptible (SIS) epidemic model describing data dissemination in opportunistic networks with heterogeneous setting of transmission parameters. We obtained the estimation of the final epidemic size assuming that amount of data transferred between network nodes possesses a Pareto distribution, implying scale-free properties. In this context, more heterogeneity in susceptibility means the less severe epidemic progression, and, on the contrary, more heterogeneity in infectivity leads to more severe epidemics -- assuming that the other parameter (either heterogeneity or susceptibility) stays fixed. The results are general enough and can be useful in general epidemic theory for estimating the epidemic progression for diseases with no significant acquired immunity -- in the cases where Pareto distribution holds.

cs.SI↗

arXiv2014

Modelling supported driving as an optimal control cycle: Framework and model characteristics

Driver assistance systems support drivers in operating vehicles in a safe, comfortable and efficient way, and thus may induce changes in traffic flow characteristics. This paper puts forward a receding horizon control framework to model driver assistance and cooperative systems. The accelerations of automated vehicles are controlled to optimise a cost function, assuming other vehicles driving at stationary conditions over a prediction horizon. The flexibility of the framework is demonstrated with controller design of Adaptive Cruise Control (ACC) and Cooperative ACC (C-ACC) systems. The proposed ACC and C-ACC model characteristics are investigated analytically, with focus on equilibrium solutions and stability properties. The proposed ACC model produces plausible human car-following behaviour and is unconditionally locally stable. By careful tuning of parameters, the ACC model generates similar stability characteristics as human driver models. The proposed C-ACC model results in convective downstream and absolute string instability, but not convective upstream string instability observed in human-driven traffic and in the ACC model. The control framework and analytical results provide insights into the influences of ACC and C-ACC systems on traffic flow operations.

physics.soc-ph↗

arXiv2014

Why Do You Spread This Message? Understanding Users Sentiment in Social Media Campaigns

Twitter has been increasingly used for spreading messages about campaigns. Such campaigns try to gain followers through their Twitter accounts, influence the followers and spread messages through them. In this paper, we explore the relationship between followers sentiment towards the campaign topic and their rate of retweeting of messages generated by the campaign. Our analysis with followers of multiple social-media campaigns found statistical significant correlations between such sentiment and retweeting rate. Based on our analysis, we have conducted an online intervention study among the followers of different social-media campaigns. Our study shows that targeting followers based on their sentiment towards the campaign can give higher retweet rate than a number of other baseline approaches.

cs.SI↗

arXiv2014

Problem Complexity in Parallel Problem Solving

Recent works examine the relationship between the communication structure and the performance of a group in a problem solving task. Some conclude that inefficient communication networks with long paths outperform efficient networks on the long run. Others find no influence of the network topology on group performance. We contribute to this discussion by examining the role of problem complexity. In particular, we study whether and how the complexity of the problem at hand moderates the influence of the communication network on group performance. Results obtained from multi-agent modelling suggest that problem complexity indeed has an influence. We observe an influence of the network only for problems of moderate difficulty. For easier or harder problems, the influence of network topology becomes weaker or irrelevant, which offers a possible explanation for inconsistencies in the literature.

cs.SI↗

arXiv2014

Do Bitcoins make the world go round? On the dynamics of competing crypto-currencies

Bitcoins have emerged as a possible competitor to usual currencies, but other crypto-currencies have likewise appeared as competitors to the Bitcoin currency. The expanding market of crypto-currencies now involves capital equivalent to $10^{10}$ US Dollars, providing academia with an unusual opportunity to study the emergence of value. Here we show that the Bitcoin currency in itself is not special, but may rather be understood as the contemporary dominating crypto-currency that may well be replaced by other currencies. We suggest that perception of value in a social system is generated by a voter-like dynamics, where fashions form and disperse even in the case where information is only exchanged on a pairwise basis between agents.

physics.soc-ph↗

arXiv2014

Quantifying Information Overload in Social Media and its Impact on Social Contagions

Information overload has become an ubiquitous problem in modern society. Social media users and microbloggers receive an endless flow of information, often at a rate far higher than their cognitive abilities to process the information. In this paper, we conduct a large scale quantitative study of information overload and evaluate its impact on information dissemination in the Twitter social media site. We model social media users as information processing systems that queue incoming information according to some policies, process information from the queue at some unknown rates and decide to forward some of the incoming information to other users. We show how timestamped data about tweets received and forwarded by users can be used to uncover key properties of their queueing policies and estimate their information processing rates and limits. Such an understanding of users' information processing behaviors allows us to infer whether and to what extent users suffer from information overload. Our analysis provides empirical evidence of information processing limits for social media users and the prevalence of information overloading. The most active and popular social media users are often the ones that are overloaded. Moreover, we find that the rate at which users receive information impacts their processing behavior, including how they prioritize information from different sources, how much information they process, and how quickly they process information. Finally, the susceptibility of a social media user to social contagions depends crucially on the rate at which she receives information. An exposure to a piece of information, be it an idea, a convention or a product, is much less effective for users that receive information at higher rates, meaning they need more exposures to adopt a particular contagion.

cs.SI↗

arXiv2014

Network Weirdness: Exploring the Origins of Network Paradoxes

Social networks have many counter-intuitive properties, including the "friendship paradox" that states, on average, your friends have more friends than you do. Recently, a variety of other paradoxes were demonstrated in online social networks. This paper explores the origins of these network paradoxes. Specifically, we ask whether they arise from mathematical properties of the networks or whether they have a behavioral origin. We show that sampling from heavy-tailed distributions always gives rise to a paradox in the mean, but not the median. We propose a strong form of network paradoxes, based on utilizing the median, and validate it empirically using data from two online social networks. Specifically, we show that for any user the majority of user's friends and followers have more friends, followers, etc. than the user, and that this cannot be explained by statistical properties of sampling. Next, we explore the behavioral origins of the paradoxes by using the shuffle test to remove correlations between node degrees and attributes. We find that paradoxes for the mean persist in the shuffled network, but not for the median. We demonstrate that strong paradoxes arise due to the assortativity of user attributes, including degree, and correlation between degree and attribute.

cs.SI↗

arXiv2014

Where Businesses Thrive: Predicting the Impact of the Olympic Games on Local Retailers through Location-based Services Data

The Olympic Games are an important sporting event with notable consequences for the general economic landscape of the host city. Traditional economic assessments focus on the aggregated impact of the event on the national income, but fail to provide micro-scale insights on why local businesses will benefit from the increased activity during the Games. In this paper we provide a novel approach to modeling the impact of the Olympic Games on local retailers by analyzing a dataset mined from a large location-based social service, Foursquare. We hypothesize that the spatial positioning of businesses as well as the mobility trends of visitors are primary indicators of whether retailers will rise their popularity during the event. To confirm this we formulate a retail winners prediction task in the context of which we evaluate a set of geographic and mobility metrics. We find that the proximity to stadiums, the diversity of activity in the neighborhood, the nearby area sociability, as well as the probability of customer flows from and to event places such as stadiums and parks are all vital factors. Through supervised learning techniques we demonstrate that the success of businesses hinges on a combination of both geographic and mobility factors. Our results suggest that location-based social networks, where crowdsourced information about the dynamic interaction of users with urban spaces becomes publicly available, present an alternative medium to assess the economic impact of large scale events in a city.

cs.SI↗

arXiv2014

The Call of the Crowd: Event Participation in Location-based Social Services

Understanding the social and behavioral forces behind event participation is not only interesting from the viewpoint of social science, but also has important applications in the design of personalized event recommender systems. This paper takes advantage of data from a widely used location-based social network, Foursquare, to analyze event patterns in three metropolitan cities. We put forward several hypotheses on the motivating factors of user participation and confirm that social aspects play a major role in determining the likelihood of a user to participate in an event. While an explicit social filtering signal accounting for whether friends are attending dominates the factors, the popularity of an event proves to also be a strong attractor. Further, we capture an implicit social signal by performing random walks in a high dimensional graph that encodes the place type preferences of friends and that proves especially suited to identify relevant niche events for users. Our findings on the extent to which the various temporal, spatial and social aspects underlie users' event preferences lead us to further hypothesize that a combination of factors better models users' event interests. We verify this through a supervised learning framework. We show that for one in three users in London and one in five users in New York and Chicago it identifies the exact event the user would attend among the pool of suggestions.

cs.SI↗