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arXiv2013

Meteors in the Maori Astronomical Traditions of New Zealand

We review the literature for perceptions of meteors in the Maori cultures of New Zealand. We examine representations of meteors in religion, story, and ceremony. We find that meteors are sometimes personified as gods or children, or are seen as omens of death and destruction. The stories we found highlight the broad perception of meteors found throughout the Maori culture and demonstrate that some early scholars conflated the terms comet and meteor.

physics.hist-ph↗

arXiv2013

The Role of Trends in Evolving Networks

Modeling complex networks has been the focus of much research for over a decade. Preferential attachment (PA) is considered a common explanation to the self organization of evolving networks, suggesting that new nodes prefer to attach to more popular nodes. The PA model results in broad degree distributions, found in many networks, but cannot explain other common properties such as: The growth of nodes arriving late and Clustering (community structure). Here we show that when the tendency of networks to adhere to trends is incorporated into the PA model, it can produce networks with such properties. Namely, in trending networks, newly arriving nodes may become central at random, forming new clusters. In particular, we show that when the network is young it is more susceptible to trends, but even older networks may have trendy new nodes that become central in their structure. Alternatively, networks can be seen as composed of two parts: static, governed by a power law degree distribution, and a dynamic part governed by trends, as we show on Wiki pages. Our results also show that the arrival of trending new nodes not only creates new clusters, but also has an effect on the relative importance and centrality of all other nodes in the network. This can explain a variety of real world networks in economics, social and online networks, and cultural networks. Products popularity, formed by the network of people's opinions, exhibit these properties. Some lines of products are increasingly susceptible to trends and hence to shifts in popularity, while others are less trendy and hence more stable. We believe that our findings have a big impact on our understanding of real networks.

physics.soc-ph↗

arXiv2013

Social Media and Information Overload: Survey Results

A UK-based online questionnaire investigating aspects of usage of user-generated media (UGM), such as Facebook, LinkedIn and Twitter, attracted 587 participants. Results show a high degree of engagement with social networking media such as Facebook, and a significant engagement with other media such as professional media, microblogs and blogs. Participants who experience information overload are those who engage less frequently with the media, rather than those who have fewer posts to read. Professional users show different behaviours to social users. Microbloggers complain of information overload to the greatest extent. Two thirds of Twitter-users have felt that they receive too many posts, and over half of Twitter-users have felt the need for a tool to filter out the irrelevant posts. Generally speaking, participants express satisfaction with the media, though a significant minority express a range of concerns including information overload and privacy.

cs.SI↗

arXiv2013

Predictability of social interactions

The ability to predict social interactions between people has profound applications including targeted marketing and prediction of information diffusion and disease propagation. Previous work has shown that the location of an individual at any given time is highly predictable. This study examines the predictability of social interactions between people to determine whether interaction patterns are similarly predictable. I find that the locations and times of interactions for an individual are highly predictable; however, the other person the individual interacts with is less predictable. Furthermore, I show that knowledge of the locations and times of interactions has almost no effect on the predictability of the other person. Finally I demonstrate that a simple Markov chain model is able to achieve close to the upper bound in terms of predicting the next person with whom a given individual will interact.

cs.SI↗

arXiv2013

Personalized Email Community Detection using Collaborative Similarity Measure

Email service providers have employed many email classification and prioritization systems over the last decade to improve their services. In order to assist email services, we propose a personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We extract the personalized social graph from a set of emails by uniquely leveraging each node with communication behavior. Subsequently, collaborative similarity measure (CSM) based intra-graph clustering approach detects personalized communities. The empirical analysis shows effectiveness of the resultant communities in terms of evaluation measures, i.e. density, entropy and f-measure. Moreover, email strainer, dynamic group prediction, and fraudulent account detection are suggested as the potential applications from both the service provider and user's point of view.

cs.SI↗

arXiv2013

The verification of virtual community members socio-demographic profile

This article considers the current problem of investigation and development of the method of web-members' socio-demographic characteristics' profile validation based on analysis of socio-demographic characteristics. The topicality of the paper is determined by the necessity to identify the web-community member by means of computer-linguistic analysis of their information track (all information about web-community members, which posted on the Internet). The formal model of basic socio-demographic characteristics of virtual communities' member is formed. The algorithm of these characteristics verification is developed.

cs.CY↗

arXiv2013

Qualified Trust, not Surveillance, is the Basis of a Stable Society

Peaceful citizens and hard-working taxpayers are under government surveillance. Confidential communication of journalists is intercepted. Civilians are killed by drones, without a chance to prove their innocence. How could it come that far? And what are the alternatives?

physics.soc-ph↗

arXiv2013

The Discrete-Continuous Logic and its possible quantum realizations

We propose a new version of generalized probabilistic propositional logic, namely, discrete-continuous logic (DCL) in which every generalized proposition (GP) is represented as 2x2 nondiagonal positive matrix with unit trace. We demonstrate that on the set of propositions of this kind one can define both the discrete logical operations (connectives) such as negation and strong logical disjunction and in addition one parameter group of continuous operations (logical rotations). We prove that an arbitrary classical proposition (which in this logic is represented by the purely diagonal matrix) can be considered as the result of strong disjunction of two identical GP. This fact gives one a good reason to presume the DCL as a prime logical substructure underlying to ordinary propositional logic, which is recorded by our consciousness. We believe that proposed version of DCL will find many applications both in physics (quantum logic) and also in cognitive sciences (mental imagery) for better understanding of the pecular nature of mental brain operations.

physics.soc-ph↗

arXiv2013

Analysis of data in the form of graphs

We discuss the problem of extending data mining approaches to cases in which data points arise in the form of individual graphs. Being able to find the intrinsic low-dimensionality in ensembles of graphs can be useful in a variety of modeling contexts, especially when coarse-graining the detailed graph information is of interest. One of the main challenges in mining graph data is the definition of a suitable pairwise similarity metric in the space of graphs. We explore two practical solutions to solving this problem: one based on finding subgraph densities, and one using spectral information. The approach is illustrated on three test data sets (ensembles of graphs); two of these are obtained from standard graph generating algorithms, while the graphs in the third example are sampled as dynamic snapshots from an evolving network simulation.

physics.data-an↗

arXiv2013

UDC in Action

The UDC (Universal Decimal Classification) is not only a classification language with a long history; it also presents a complex cognitive system worthy of the attention of complexity theory. The elements of the UDC: classes, auxiliaries, and operations are combined into symbolic strings, which in essence represent a complex networks of concepts. This network forms a backbone of ordering of knowledge and at the same time allows expression of different perspectives on various products of human knowledge production. In this paper we look at UDC strings derived from the holdings of libraries. In particular we analyse the subject headings of holdings of the university library in Leuven, and an extraction of UDC numbers from the OCLC WorldCat. Comparing those sets with the Master Reference File, we look into the length of strings, the occurrence of different auxiliary signs, and the resulting connections between UDC classes. We apply methods and representations from complexity theory. Mapping out basic statistics on UDC classes as used in libraries from a point of view of complexity theory bears different benefits. Deploying its structure could serve as an overview and basic information for users among the nature and focus of specific collections. A closer view into combined UDC numbers reveals the complex nature of the UDC as an example for a knowledge ordering system, which deserves future exploration from a complexity theoretical perspective.

cs.DL↗

arXiv2013

Fourier Analysis of an Expanded Gravity Model for Spatio-Temporal Interactions

Fourier analysis and cross-correlation function are successfully applied to improving the conventional gravity model of interaction between cities by introducing a time variable to the attraction measures (e.g., city sizes). The traditional model assumes spatial interaction as instantaneous, while the new model considers the interaction as a temporal process and measures it as an aggregation over a period of time. By doing so, the new model not only is more theoretically sound, but also enables us to integrate the analysis of temporal process into spatial interaction modeling. Based on cross-correlation function, the developed model is calibrated by Fourier analysis techniques, and the computation process is demonstrated in four steps. The paper uses a simple case study to illustrate the approach to modeling the interurban interaction, and highlight the relationship between the new model and the conventional gravity model.

physics.soc-ph↗

arXiv2013

Redefining the Attraction Measure, Scaling Exponent and Impedance Function of the Gravity Model

The attraction measure, scaling exponent, and impedance function of the gravity model are redefined using the concepts from fractals and spatial complexity. Firstly, the attraction measure of spatial interaction in human systems is defined by the product of traffic inflow and outflow. Based on the new definition, the gravity model originating from the Newtonian analogy is differentiated from Wilson's spatial interaction model deriving from entropy-maximizing principle. Secondly, the scaling exponent of the gravity model based on the inverse distance relationship is revealed to be the ratio of the fractal dimension of networks of cities to that of hierarchies of cities. The value of the scaling exponent is then shown to approach to 2, which corresponds to the value of the power exponent of the law of gravity in classical physics. Thirdly, the inverse power function is demonstrated to be more acceptable than the negative exponential function as an impedance function of the gravity model. The limits of application of different gravity models based on different impedance functions are brought to light. Applying the expanded theory and models to China's interregional flows yields satisfying results, which in turn lend further support to the expanded theory and models.

physics.soc-ph↗

arXiv2013

TwitterCrowds: Techniques for Exploring Topic and Sentiment in Microblogging Data

Analysts and social scientists in the humanities and industry require techniques to help visualize large quantities of microblogging data. Methods for the automated analysis of large scale social media data (on the order of tens of millions of tweets) are widely available, but few visualization techniques exist to support interactive exploration of the results. In this paper, we present extended descriptions of ThemeCrowds and SentireCrowds, two tag-based visualization techniques for this data. We subsequently introduce a new list equivalent for both of these techniques and present a number of case studies showing them in operation. Finally, we present a formal user study to evaluate the effectiveness of these list interface equivalents when comparing them to ThemeCrowds and SentireCrowds. We find that discovering topics associated with areas of strong positive or negative sentiment is faster when using a list interface. In terms of user preference, multilevel tag clouds were found to be more enjoyable to use. Despite both interfaces being usable for all tested tasks, we have evidence to support that list interfaces can be more efficient for tasks when an appropriate ordering is known beforehand.

cs.SI↗

arXiv2013

A surrogate for networks -- How scale-free is my scale-free network?

Complex networks are now being studied in a wide range of disciplines across science and technology. In this paper we propose a method by which one can probe the properties of experimentally obtained network data. Rather than just measuring properties of a network inferred from data, we aim to ask how typical is that network? What properties of the observed network are typical of all such scale free networks, and which are peculiar? To do this we propose a series of methods that can be used to generate statistically likely complex networks which are both similar to the observed data and also consistent with an underlying null-hypothesis -- for example a particular degree distribution. There is a direct analogy between the approach we propose here and the surrogate data methods applied to nonlinear time series data.

physics.soc-ph↗

arXiv2013

Social Network Dynamics in a Massive Online Game: Network Turnover, Non-densification, and Team Engagement in Halo Reach

Online multiplayer games are a popular form of social interaction, used by hundreds of millions of individuals. However, little is known about the social networks within these online games, or how they evolve over time. Understanding human social dynamics within massive online games can shed new light on social interactions in general and inform the development of more engaging systems. Here, we study a novel, large friendship network, inferred from nearly 18 billion social interactions over 44 weeks between 17 million individuals in the popular online game Halo: Reach. This network is one of the largest, most detailed temporal interaction networks studied to date, and provides a novel perspective on the dynamics of online friendship networks, as opposed to mere interaction graphs. Initially, this network exhibits strong structural turnover and decays rapidly from a peak size. In the following period, however, both network size and turnover stabilize, producing a dynamic structural equilibrium. In contrast to other studies, we find that the Halo friendship network is non-densifying: both the mean degree and the average pairwise distance are stable, suggesting that densification cannot occur when maintaining friendships is costly. Finally, players with greater long-term engagement exhibit stronger local clustering, suggesting a group-level social engagement process. These results demonstrate the utility of online games for studying social networks, shed new light on empirical temporal graph patterns, and clarify the claims of universality of network densification.

cs.SI↗

arXiv2013

Exploiting Cellular Data for Disease Containment and Information Campaigns Strategies in Country-Wide Epidemics

Human mobility is one of the key factors at the basis of the spreading of diseases in a population. Containment strategies are usually devised on movement scenarios based on coarse-grained assumptions. Mobility phone data provide a unique opportunity for building models and defining strategies based on very precise information about the movement of people in a region or in a country. Another very important aspect is the underlying social structure of a population, which might play a fundamental role in devising information campaigns to promote vaccination and preventive measures, especially in countries with a strong family (or tribal) structure. In this paper we analyze a large-scale dataset describing the mobility and the call patterns of a large number of individuals in Ivory Coast. We present a model that describes how diseases spread across the country by exploiting mobility patterns of people extracted from the available data. Then, we simulate several epidemics scenarios and we evaluate mechanisms to contain the epidemic spreading of diseases, based on the information about people mobility and social ties, also gathered from the phone call data. More specifically, we find that restricting mobility does not delay the occurrence of an endemic state and that an information campaign based on one-to-one phone conversations among members of social groups might be an effective countermeasure.

cs.SI↗

arXiv2013

Wisdom of Crowds Algorithm for Stock Market Predictions

In this paper we present a mathematical model for collaborative filtering implementation in stock market predictions. In popular literature collaborative filtering, also known as Wisdom of Crowds, assumes that group has a greater knowledge than the individual while each individual can improve group's performance by its specific information input. There are commercially available tools for collaborative stock market predictions and patent protected web-based software solutions. Mathematics that lies behind those algorithms is not disclosed in the literature, so the presented model and algorithmic implementation are the main contributions of this work.

cs.SI↗

arXiv2013

How are mortality rates affected by population density?

Biologists have found that the death rate of cells in culture depends upon their spatial density. Permanent "Stay alive" signals from their neighbours seem to prevent them from dying. In a previous paper (Wang et al. 2013) we gave evidence for a density effect for ants. In this paper we examine whether there is a similar effect in human demography. We find that although there is no observable relationship between population density and overall death rates, there is a clear relationship between density and the death rates of young age-groups. Basically their death rates decrease with increasing density. However, this relationship breaks down around 300 inhabitants per square kilometre. Above this threshold the death rates remains fairly constant. The same density effect is observed in Canada, France, Japan and the United States. We also observe a striking parallel between the density effect and the so-called marital status effect in the sense that they both lead to higher suicide rates and are both enhanced for younger age-groups. However, it should be noted that the strength of the density effect is only a fraction of the strength of the marital status effect. In spite of the fact that this parallel does not give us an explanation by itself, it invites us to focus on explanations that apply to both effects. In this light the "Stay alive" paradigm set forth by Prof. Martin Raff appears as a natural interpretation. It can be seen as an extension of the "social ties" framework proposed at the end of the 19th century by the sociologist Emile Durkheim in his study about suicide.

physics.soc-ph↗