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arXiv2014

Co-Evolution of Friendship and Publishing in Online Blogging Social Networks

In the past decade, blogging web sites have become more sophisticated and influential than ever. Much of this sophistication and influence follows from their network organization. Blogging social networks (BSNs) allow individual bloggers to form contact lists, subscribe to other blogs, comment on blog posts, declare interests, and participate in collective blogs. Thus, a BSN is a bimodal venue, where users can engage in publishing (post) as well as in social (make friends) activities. In this paper, we study the co-evolution of both activities. We observed a significant positive correlation between blogging and socializing. In addition, we identified a number of user archetypes that correspond to "mainly bloggers," "mainly socializers," etc. We analyzed a BSN at the level of individual posts and changes in contact lists and at the level of trajectories in the friendship-publishing space. Both approaches produced consistent results: the majority of BSN users are passive readers; publishing is the dominant active behavior in a BSN; and social activities complement blogging, rather than compete with it.

cs.SI↗
arXiv2014

Contexts of diffusion: Adoption of research synthesis in Social Work and Women's Studies

Texts reveal the subjects of interest in research fields, and the values, beliefs, and practices of researchers. In this study, texts are examined through bibliometric mapping and topic modeling to provide a birds eye view of the social dynamics associated with the diffusion of research synthesis methods in the contexts of Social Work and Women's Studies. Research synthesis texts are especially revealing because the methods, which include meta-analysis and systematic review, are reliant on the availability of past research and data, sometimes idealized as objective, egalitarian approaches to research evaluation, fundamentally tied to past research practices, and performed with the goal informing future research and practice. This study highlights the co-influence of past and subsequent research within research fields; illustrates dynamics of the diffusion process; and provides insight into the cultural contexts of research in Social Work and Women's Studies. This study suggests the potential to further develop bibliometric mapping and topic modeling techniques to inform research problem selection and resource allocation.

cs.SI↗
arXiv2014

Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks

Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with common connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying communities of densely connected nodes in undirected networks. In this paper we develop a novel overlapping community detection method that scales to networks of millions of nodes and edges and advances research along two dimensions: the connectivity structure of communities, and the use of edge directedness for community detection. First, we extend traditional definitions of network communities by building on the observation that nodes can be densely interlinked in two different ways: In cohesive communities nodes link to each other, while in 2-mode communities nodes link in a bipartite fashion, where links predominate between the two partitions rather than inside them. Our method successfully detects both 2-mode as well as cohesive communities, that may also overlap or be hierarchically nested. Second, while most existing community detection methods treat directed edges as though they were undirected, our method accounts for edge directions and is able to identify novel and meaningful community structures in both directed and undirected networks, using data from social, biological, and ecological domains.

cs.SI↗
arXiv2014

Microeconomic Structure determines Macroeconomic Dynamics. Aoki defeats the Representative Agent

Masanao Aoki developed a new methodology for a basic problem of economics: deducing rigorously the macroeconomic dynamics as emerging from the interactions of many individual agents. This includes deduction of the fractal / intermittent fluctuations of macroeconomic quantities from the granularity of the mezo-economic collective objects (large individual wealth, highly productive geographical locations, emergent technologies, emergent economic sectors) in which the micro-economic agents self-organize. In particular, we present some theoretical predictions, which also met extensive validation from empirical data in a wide range of systems: - The fractal Levy exponent of the stock market index fluctuations equals the Pareto exponent of the investors wealth distribution. The origin of the macroeconomic dynamics is therefore found in the granularity induced by the wealth / capital of the wealthiest investors. - Economic cycles consist of a Schumpeter 'creative destruction' pattern whereby the maxima are cusp-shaped while the minima are smooth. In between the cusps, the cycle consists of the sum of 2 'crossing exponentials': one decaying and the other increasing. This unification within the same theoretical framework of short term market fluctuations and long term economic cycles offers the perspective of a genuine conceptual synthesis between micro- and macroeconomics. Joining another giant of contemporary science - Phil Anderson - Aoki emphasized the role of rare, large fluctuations in the emergence of macroeconomic phenomena out of microscopic interactions and in particular their non self-averaging, in the language of statistical physics. In this light, we present a simple stochastic multi-sector growth model.

q-fin.GN↗
arXiv2014

Kinetic description of optimal control problems and applications to opinion consensus

In this paper an optimal control problem for a large system of interacting agents is considered using a kinetic perspective. As a prototype model we analyze a microscopic model of opinion formation under constraints. For this problem a Boltzmann-type equation based on a model predictive control formulation is introduced and discussed. In particular, the receding horizon strategy permits to embed the minimization of suitable cost functional into binary particle interactions. The corresponding Fokker-Planck asymptotic limit is also derived and explicit expressions of stationary solutions are given. Several numerical results showing the robustness of the present approach are finally reported.

math.OC↗
arXiv2014

Exploring the Relationship between Membership Turnover and Productivity in Online Communities

One of the more disruptive reforms associated with the modern Internet is the emergence of online communities working together on knowledge artefacts such as Wikipedia and OpenStreetMap. Recently it has become clear that these initiatives are vulnerable because of problems with membership turnover. This study presents a longitudinal analysis of 891 WikiProjects where we model the impact of member turnover and social capital losses on project productivity. By examining social capital losses we attempt to provide a more nuanced analysis of member turnover. In this context social capital is modelled from a social network perspective where the loss of more central members has more impact. We find that only a small proportion of WikiProjects are in a relatively healthy state with low levels of membership turnover and social capital losses. The results show that the relationship between social capital losses and project performance is U-shaped, and that member withdrawal has significant negative effect on project outcomes. The results also support the mediation of turnover rate and network density on the curvilinear relationship.

cs.SI↗
arXiv2014

When is it Biased? Assessing the Representativeness of Twitter's Streaming API

Twitter has captured the interest of the scientific community not only for its massive user base and content, but also for its openness in sharing its data. Twitter shares a free 1% sample of its tweets through the "Streaming API", a service that returns a sample of tweets according to a set of parameters set by the researcher. Recently, research has pointed to evidence of bias in the data returned through the Streaming API, raising concern in the integrity of this data service for use in research scenarios. While these results are important, the methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. In this work we tackle the problem of finding sample bias without the need for "gold standard" Firehose data. Namely, we focus on finding time periods in the Streaming API data where the trend of a hashtag is significantly different from its trend in the true activity on Twitter. We propose a solution that focuses on using an open data source to find bias in the Streaming API. Finally, we assess the utility of the data source in sparse data situations and for users issuing the same query from different regions.

cs.SI↗
arXiv2014

Online Dating Recommendations: Matching Markets and Learning Preferences

Recommendation systems for online dating have recently attracted much attention from the research community. In this paper we proposed a two-side matching framework for online dating recommendations and design an LDA model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large online dating website shows that two-sided matching improves significantly the rate of successful matches by as much as 45%. Finally, using simulated matchings we show that the the LDA model can correctly capture user preferences.

cs.SI↗
arXiv2014

Classifying Latent Infection States in Complex Networks

Algorithms for identifying the infection states of nodes in a network are crucial for understanding and containing infections. Often, however, only a relatively small set of nodes have a known infection state. Moreover, the length of time that each node has been infected is also unknown. This missing data -- infection state of most nodes and infection time of the unobserved infected nodes -- poses a challenge to the study of real-world cascades. In this work, we develop techniques to identify the latent infected nodes in the presence of missing infection time-and-state data. Based on the likely epidemic paths predicted by the simple susceptible-infected epidemic model, we propose a measure (Infection Betweenness) for uncovering these unknown infection states. Our experimental results using machine learning algorithms show that Infection Betweenness is the most effective feature for identifying latent infected nodes.

cs.SI↗
arXiv2014

A high-reproducibility and high-accuracy method for automated topic classification

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent search, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state-of-the-art in topic classification. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results which are not accurate in inferring the most suitable model parameters. Adapting approaches for community detection in networks, we propose a new algorithm which displays high-reproducibility and high-accuracy, and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure. Our algorithm promises to make "big data" text analysis systems more reliable.

stat.ML↗
arXiv2014

A Game Theoretic Analysis of Collaboration in Wikipedia

Peer production projects such as Wikipedia or open-source software development allow volunteers to collectively create knowledge based products. The inclusive nature of such projects poses difficult challenges for ensuring trustworthiness and combating vandalism. Prior studies in the area deal with descriptive aspects of peer production, failing to capture the idea that while contributors collaborate, they also compete for status in the community and for imposing their views on the product. In this paper we investigate collaborative authoring in Wikipedia where contributors append and overwrite previous contributions to a page. We assume that a contributors goal is to maximize ownership of content sections such that content owned (or originated) by her survived the most recent revision of the page. We model contributors interactions to increase their content ownership as a noncooperative game where a players utility is associated with content owned and cost is a function of effort expended. Our results capture several real life aspects of contributors interactions within peer production projects. We show that at the Nash equilibrium there is an inverse relationship between the effort required to make a contribution and the survival of a contributors content. In other words majority of the content that survives is necessarily contributed by experts who expend relatively less effort than non experts. An empirical analysis of Wikipedia articles provides support for our models predictions. Implications for research and practice are discussed in the context of trustworthy collaboration as well as vandalism.

cs.GT↗
arXiv2014

How to Apply Assignment Methods that were Developed for Vehicular Traffic to Pedestrian Microsimulations

Applying assignment methods to compute user-equilibrium route choice is very common in traffic planning. It is common sense that vehicular traffic arranges in a user-equilibrium based on generalized costs in which travel time is a major factor. Surprisingly travel time has not received much attention for the route choice of pedestrians. In microscopic simulations of pedestrians the vastly dominating paradigm for the computation of the preferred walking direction is set into the direction of the (spatially) shortest path. For situations where pedestrians have travel time as primary determinant for their walking behavior it would be desirable to also have an assignment method in pedestrian simulations. To apply existing (road traffic) assignment methods with simulations of pedestrians one has to reduce the nondenumerably many possible pedestrian trajectories to a small subset of routes which represent the main, relevant, and significantly distinguished routing alternatives. All except one of these routes will mark detours, i.e. not the shortest connection between origin and destination. The proposed assignment method is intended to work with common operational models of pedestrian dynamics. These - as mentioned before - usually send pedestrians into the direction of the spatially shortest path. Thus, all detouring routes have to be equipped with intermediate destinations, such that pedestrians can do a detour as a piecewise connection of segments on which they walk into the direction of the shortest path. One has then to take care that the transgression from one segment to the following one no artifacts are introduced into the pedestrian trajectory.

cs.CE↗
arXiv2014

Network Sampling Based on NN Representatives

The amount of large-scale real data around us increase in size very quickly and so does the necessity to reduce its size by obtaining a representative sample. Such sample allows us to use a great variety of analytical methods, whose direct application on original data would be infeasible. There are many methods used for different purposes and with different results. In this paper we outline a simple and straightforward approach based on analyzing the nearest neighbors (NN) that is generally applicable. This feature is illustrated on experiments with weighted networks and vector data. The properties of the representative sample show that the presented approach maintains very well internal data structures (e.g. clusters and density). Key technical parameters of the approach is low complexity and high scalability. This allows the application of this approach to the area of big data.

cs.SI↗
arXiv2014

Women in Italian astronomy

This document gives some quantitative facts about the role of women in Italian astronomy. More than 26% of Italian IAU members are women: this is the largest fraction among the world leading countries in astronomy. Most of this high fraction is due to their presence in INAF, where women make up 32% of the research staff (289 out of 908) and 40% of the technical/administrative staff (173 out of 433); the percentage is slightly lower among permanent research staff (180 out of 599, about 30%). The presence of women is lower in the Universities (27 out of 161, about 17%, among staff). In spite of these (mildly) positive facts, we notice that similarly to other countries (e.g. USA and Germany) career prospects for Italian astronomers are clearly worse for women than for men. Within INAF, the fraction of women is about 35-40% among non-permanent position, 36% for Investigators, 17% for Associato/Primo Ricercatore, and only 13% among Ordinario/Dirigente di Ricerca. The situation is even worse at University (only 6% of Professore Ordinario are women). We found that similar trends are also present if researchers are ordered according to citation rather than position: for instance, women make up only 15% among the 100 most cited astronomers working in Italy, a percentage which is however twice that over all Europe. A similar fraction is found among first authors of most influential papers, which cannot be explained as a residual of a lower female presence in the past. We conclude that implicit sex discrimination factors probably dominate over explicit ones and are still strongly at work. Finally, we discuss the possible connection between the typical career pattern and these factors.

astro-ph.IM↗
arXiv2014

Statistical Signs of Social Influence on Suicides

Certain currents in sociology consider society as being composed of autonomous individuals with independent psychologies. Others, however, deem our actions as strongly influenced by the accepted standards of social behavior. The later view was central to the positivist conception of society when in 1887 Émile Durkheim published his monograph Suicide (Durkheim, 1897). By treating the suicide as a social fact, Durkheim envisaged that suicide rates should be determined by the connections (or the lack of them) between people and society. Under the same framework, Durkheim considered that crime is bound up with the fundamental conditions of all social life and serves a social function. In this sense, and regardless of its extremely deviant nature, crime events are somehow capable to release certain social tensions and so have a purging effect in society. The social effect on the occurrence of homicides has been previously substantiated (Bettencourt et al., 2007; Alves et al., 2013), and confirmed here, in terms of a superlinear scaling relation: by doubling the population of a Brazilian city results in an average increment of 135 % in the number of homicides, rather than the expected isometric increase of 100 %, as found, for example, for the mortality due to car crashes. Here we present statistical signs of the social influence on the suicide occurrence in cities. Differently from homicides (superlinear) and fatal events in car crashes (isometric), we find sublinear scaling behavior between the number of suicides and city population, with allometric power-law exponents, $β= 0.836 \pm 0.009$ and $0.870 \pm 0.002$, for all cities in Brazil and US, respectively. The fact that the frequency of suicides is disproportionately small for larger cities reveals a surprisingly beneficial aspect of living and interacting in larger and more complex social networks.

stat.AP↗
arXiv2014

Two Steps to Obfuscation

This note addresses the historical antecedents of the 1998 PageRank measure of centrality. An identity relation links it to 1990-1991 models of Friedkin and Johnsen.

cs.SI↗
arXiv2014

To Go Viral

Mathematical models are validated against empirical data, while examining potential indicators for an online video that went viral. We revisit some concepts of infectious disease modeling (e.g. reproductive number) and we comment on the role of model parameters that interplay in the spread of innovations. The dataset employed here provides strong evidence that the number of online views is governed by exponential growth patterns, explaining a common feature of viral videos.

physics.soc-ph↗