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At least 649 records · Page 36Linked to original sources

arXiv2013

(Failure of the) Wisdom of the crowds in an endogenous opinion dynamics model with multiply biased agents

We study an endogenous opinion (or, belief) dynamics model where we endogenize the social network that models the link (`trust') weights between agents. Our network adjustment mechanism is simple: an agent increases her weight for another agent if that agent has been close to truth (whence, our adjustment criterion is `past performance'). Moreover, we consider multiply biased agents that do not learn in a fully rational manner but are subject to persuasion bias - they learn in a DeGroot manner, via a simple `rule of thumb' - and that have biased initial beliefs. In addition, we also study this setup under conformity, opposition, and homophily - which are recently suggested variants of DeGroot learning in social networks - thereby taking into account further biases agents are susceptible to. Our main focus is on crowd wisdom, that is, on the question whether the so biased agents can adequately aggregate dispersed information and, consequently, learn the true states of the topics they communicate about. In particular, we present several conditions under which wisdom fails.

cs.SI↗

arXiv2013

User-Relatedness and Community Structure in Social Interaction Networks

With social media and the according social and ubiquitous applications finding their way into everyday life, there is a rapidly growing amount of user generated content yielding explicit and implicit network structures. We consider social activities and phenomena as proxies for user relatedness. Such activities are represented in so-called social interaction networks or evidence networks, with different degrees of explicitness. We focus on evidence networks containing relations on users, which are represented by connections between individual nodes. Explicit interaction networks are then created by specific user actions, for example, when building a friend network. On the other hand, more implicit networks capture user traces or evidences of user actions as observed in Web portals, blogs, resource sharing systems, and many other social services. These implicit networks can be applied for a broad range of analysis methods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in social media. We show that there are dependencies and correlations between the networks. These allow for drawing reciprocal conclusions concerning pairs of networks, based on the assessment of structural correlations and ranking interchangeability. Additionally, we show how these inter-network correlations can be used for assessing the results of structural analysis techniques, e.g., community mining methods.

cs.SI↗

arXiv2013

Stochastic Optimization of Service Provision with Selfish Users

We develop a computationally efficient technique to solve a fairly general distributed service provision problem with selfish users and imperfect information. In particular, in a context in which the service capacity of the existing infrastructure can be partially adapted to the user load by activating just some of the service units, we aim at finding the configuration of active service units that achieves the best trade-off between maintenance (e.g.\ energetic) costs for the provider and user satisfaction. The core of our technique resides in the implementation of a belief-propagation (BP) algorithm to evaluate the cost configurations. Numerical results confirm the effectiveness of our approach.

cs.GT↗

arXiv2013

Facebook Applications' Installation and Removal: A Temporal Analysis

Facebook applications are one of the reasons for Facebook attractiveness. Unfortunately, numerous users are not aware of the fact that many malicious Facebook applications exist. To educate users, to raise users' awareness and to improve Facebook users' security and privacy, we developed a Firefox add-on that alerts users to the number of installed applications on their Facebook profiles. In this study, we present the temporal analysis of the Facebook applications' installation and removal dataset collected by our add-on. This dataset consists of information from 2,945 users, collected during a period of over a year. We used linear regression to analyze our dataset and discovered the linear connection between the average percentage change of newly installed Facebook applications and the number of days passed since the user initially installed our add-on. Additionally, we found out that users who used our Firefox add-on become more aware of their security and privacy installing on average fewer new applications. Finally, we discovered that on average 86.4% of Facebook users install an additional application every 4.2 days.

cs.SI↗

arXiv2013

Ultrametric dynamics for the closed fractal-cluster resource models

The evolutional scenario of the resource distribution in the fractal-cluster systems which is identified as an "organism" has been suggested. We propose a model in which the resource redistribution dynamics in the closed system is determined by the ultrametric structure of the system's space. Moreover, each cluster has its own character time of a transfer to the equilibrium state which is determined by the ultrametric size of the cluster. The general equation which determines this dynamics has been written. For the determined type of the resource transitions among clusters, the solution to this equation has been numerically received. The problem of the parameter's identification modeling for the real systems has been discussed.

physics.soc-ph↗

arXiv2013

EgoNet-UIUC: A Dataset For Ego Network Research

In this report, we introduce the version one of EgoNet-UIUC, which is a dataset for ego network research. The dataset contains about 230 ego networks in Linkedin, which have about 33K users (with their attributes) and 283K relationships (with their relationship types) in total. We name this dataset as EgoNet-UIUC, which stands for Ego Network Dataset from University of Illinois at Urbana-Champaign.

cs.SI↗

arXiv2013

Evaluating socio-economic state of a country analyzing airtime credit and mobile phone datasets

Reliable statistical information is important to make political decisions on a sound basis and to help measure the impact of policies. Unfortunately, statistics offices in developing countries have scarce resources and statistical censuses are therefore conducted sporadically. Based on mobile phone communications and history of airtime credit purchases, we estimate the relative income of individuals, the diversity and inequality of income, and an indicator for socioeconomic segregation for fine-grained regions of an African country. Our study shows how to use mobile phone datasets as a starting point to understand the socio-economic state of a country, which can be especially useful in countries with few resources to conduct large surveys.

cs.CY↗

arXiv2013

General Clique Percolation in Network Evolution

We introduce a general $(k,l)$ clique community, which consists of adjacent $k$-cliques sharing at least $l$ vertices with $k-1 \ge l \ge 1$. The emergence of a giant $(k,l)$ clique community indicates a $(k,l)$ clique percolation, which is studied by the largest size gap $Δ$ of the largest clique community during network evolution and the corresponding evolution step $T_c$. For a clique percolation, the averages of $Δ$ and $T_c$ and the root-mean-squares of their fluctuations have power law finite-size effects whose exponents are related to the critical exponents. The fluctuation distribution functions of $Δ$ and $T_c$ follow a finite-size scaling form. In the evolution of the Erdős-Rényi network, there are a series of $(k,l)$ clique percolation with $(k,l)=(2,1),(3,1),(3,2),(4,1),(4,2),(5,1),(4,3)$, and so on. The critical exponents of clique percolation depend on $l$, but are independent of $k$. The universality class of a $(k,l)$ clique percolation is characterized alone by $l$.

cond-mat.stat-mech↗

arXiv2013

Stochastic Modelings of Social Phenomena: Pedestrian Counter Flow and Tournaments

We present here two examples of stochastic modelings of social phenomena. The first topic is pedestrian counter flow. Two groups of model pedestrians move in opposite directions and create congestions. It will be shown that this congestion becomes worst where individuals are given certain stochastic freedom to avoid another in front compared to the case that they are bound to more strict rules. The second example model tournaments. We present here a rather unexpected feature of tournaments that the probability to reach the top position is higher than that of finishing up at lower positions for not only the number one ranked player, but also for a range of top players. This "inversion characteristics" are shown to be observed with simple mathematical model tournaments as well as in the real tournaments.

physics.soc-ph↗

arXiv2013

Comment on "Nature of the Epidemic Threshold for the Susceptible-Infected-Susceptible Dynamics in Networks" [arXiv:1305.4819]

Recently, Boguñá {\it et. al.} [Phys. Rev. Lett. {\bf 111}, 068701 (2013), arXiv:1305.4819] claimed that the epidemic threshold of the susceptible-infected-susceptible (SIS) model is zero on random scale-free (SF) networks with the small-world (SW) property. They drew the conclusion by taking into account a long-range reinfection mechanism. In this Comment, we will show that such an effect is too weak to guarantee an endemic phase with a {\em finite} fraction of infected nodes. Consequently, the epidemic threshold can be finite in random SF networks.

physics.soc-ph↗

arXiv2013

Emergent Behaviors over Signed Random Networks in Dynamical Environments

We study asymptotic dynamical patterns that emerge among a set of nodes that interact in a dynamically evolving signed random network. Node interactions take place at random on a sequence of deterministic signed graphs. Each node receives positive or negative recommendations from its neighbors depending on the sign of the interaction arcs, and updates its state accordingly. Positive recommendations follow the standard consensus update while two types of negative recommendations, each modeling a different type of antagonistic or malicious interaction, are considered. Nodes may weigh positive and negative recommendations differently, and random processes are introduced to model the time-varying attention that nodes pay to the positive and negative recommendations. Various conditions for almost sure convergence, divergence, and clustering of the node states are established. Some fundamental similarities and differences are established for the two notions of negative recommendations.

cs.SI↗

arXiv2013

Migration and Trade: A Complex-Network Approach

This paper explores the relationships between migration and trade using a complex-network approach. We show that: (i) both weighted and binary versions of the networks of international migration and trade are strongly correlated; (ii) such correlations can be mostly explained by country economic/demographic size and geographical distance; (iii) pairs of countries that are more central in the international-migration network trade more.

physics.soc-ph↗

arXiv2013

Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing

Location Based Services (LBS) provide a new perspective for spatiotemporally analyzing dynamic urban systems. Research has investigated urban dynamics using GSM (Global System for Mobile Communications), GPS (Global Positioning System), SNS (Social Networking Services) and Wi-Fi techniques. However, less attention has been paid to the analysis of urban structure (especially commuting pattern) using smart card data (SCD), which are widely available in most cities. Additionally, ubiquitous LBS data, although providing rich spatial and temporal information, lacks rich information on the social dimension, which limits its in-depth application. To bridge this gap, this paper combines bus SCD for a one-week period with a one-day household travel survey, as well as a parcel-level land use map to identify job-housing locations and commuting trip routes in Beijing. Two data forms (TRIP and PTD) are proposed, with PTD used for jobs-housing identification and TRIP used for commuting trip route identification. The results of the identification are aggregated in the bus stop and traffic analysis zone (TAZ) scales, respectively. Particularly, commuting trips from three typical residential communities to six main business zones are mapped and compared to analyze commuting patterns in Beijing. The identified commuting trips are validated on three levels by comparison with those from the survey in terms of commuting time and distance, and the positive validation results prove the applicability of our approach. Our experiment, as a first step toward enriching LBS data using conventional survey and urban GIS data, can obtain solid identification results based on rules extracted from existing surveys or censuses.

cs.SI↗

arXiv2013

Predicting Adoption Probabilities in Social Networks

In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns; yet, predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally-weighted expectation-maximization method for Naïve Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power, and that confounding factors are critical to adoption probability predictions.

cs.SI↗

arXiv2013

Maximizing the Spread of Stable Influence: Leveraging Norm-driven Moral-Motivation for Green Behavior Change in Networks

In an effort to understand why individuals choose to participate in personally-expensive pro-environmental behaviors, environmental and behavioral economists have examined a moral-motivation model in which the decision to adopt a pro-environmental behavior depends on the society-wide market share of that behavior. An increasing body of practical research on adoption of pro-environmental behavior emphasizes the importance of encouragement from local social contacts and messaging about locally-embraced norms: we respond by extending the moral-motivation model to a social networks setting. We obtain a new decision rule: an individual adopts a pro-environmental behavior if he or she observes a certain threshold of adoption within their local social neighborhood. This gives rise to a concurrent update process which describes adoption of a pro-environmental behavior spreading through a network. The original moral-motivation model corresponds to the special case of our network version in a complete graph. By improving convergence results, we formulate modest-size Integer Programs that accurately (but not efficiently) find minimum-size sets of nodes that convert the entire network, or alternately that maximize long-term adoption in the network given a limited number of nodes which may be temporarily converted. Issues of stability in determining long-term adoption are key. We give hardness of approximation results for these optimization problems. We demonstrate that there exist classes of networks which qualitatively have severely different behavior than the non-networked version, and provide preliminary computational results in in modestly-sized highly-clustered small-world networks related to the famous small-world networks of Watts and Strogatz.

cs.SI↗

arXiv2013

Collective Diffusion Over Networks: Models and Inference

Diffusion processes in networks are increasingly used to model the spread of information and social influence. In several applications in computational sustainability such as the spread of wildlife, infectious diseases and traffic mobility pattern, the observed data often consists of only aggregate information. In this work, we present new models that generalize standard diffusion processes to such collective settings. We also present optimization based techniques that can accurately learn the underlying dynamics of the given contagion process, including the hidden network structure, by only observing the time a node becomes active and the associated aggregate information. Empirically, our technique is highly robust and accurately learns network structure with more than 90% recall and precision. Results on real-world flu spread data in the US confirm that our technique can also accurately model infectious disease spread.

cs.SI↗

arXiv2013

Interdependent networks: the fragility of control

Recent work in the area of interdependent networks has focused on interactions between two systems of the same type. However, an important and ubiquitous class of systems are those involving monitoring and control, an example of interdependence between processes that are very different. In this Article, we introduce a framework for modelling distributed supervisory control in the guise of an electrical network supervised by a distributed system of control devices. The system is characterised by degrees of freedom salient to real-world systems--- namely, the number of control devices, their inherent reliability, and the topology of the control network. Surprisingly, the behavior of the system depends crucially on the reliability of control devices. When devices are completely reliable, cascade sizes are percolation controlled; the number of devices being the relevant parameter. For unreliable devices, the topology of the control network is important and can dramatically reduce the resilience of the system.

cond-mat.dis-nn↗

arXiv2013

A New Kind of Economy is Born - Social Decision-Makers Beat the "Homo Economicus"

The Internet and Social Media change our way of decision-making. We are no longer the independent decision makers we used to be. Instead, we have become networked minds, social decision-makers, more than ever before. This has several fundamental implications. First of all, our economic theories must change, and second, our economic institutions must be adapted to support the social decision-maker, the "homo socialis", rather than tailored to the perfect egoist, known as "homo economicus".

physics.soc-ph↗