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arXiv2013

Emergence of scaling in human-interest dynamics

Human behaviors are often driven by human interests. Despite intense recent efforts in exploring the dynamics of human behaviors, little is known about human-interest dynamics, partly due to the extreme difficulty in accessing the human mind from observations. However, the availability of large-scale data, such as those from e-commerce and smart-phone communications, makes it possible to probe into and quantify the dynamics of human interest. Using three prototypical "big data" sets, we investigate the scaling behaviors associated with human-interest dynamics. In particular, from the data sets we uncover power-law scaling associated with the three basic quantities: (1) the length of continuous interest, (2) the return time of visiting certain interest, and (3) interest ranking and transition. We argue that there are three basic ingredients underlying human-interest dynamics: preferential return to previously visited interests, inertial effect, and exploration of new interests. We develop a biased random-walk model, incorporating the three ingredients, to account for the observed power-law scaling relations. Our study represents the first attempt to understand the dynamical processes underlying human interest, which has significant applications in science and engineering, commerce, as well as defense, in terms of specific tasks such as recommendation and human-behavior prediction.

physics.soc-ph↗

arXiv2013

Flavor Pairing in Medieval European Cuisine: A Study in Cooking with Dirty Data

An important part of cooking with computers is using statistical methods to create new, flavorful ingredient combinations. The flavor pairing hypothesis states that culinary ingredients with common chemical flavor components combine well to produce pleasant dishes. It has been recently shown that this design principle is a basis for modern Western cuisine and is reversed for Asian cuisine. Such data-driven analysis compares the chemistry of ingredients to ingredient sets found in recipes. However, analytics-based generation of novel flavor profiles can only be as good as the underlying chemical and recipe data. Incomplete, inaccurate, and irrelevant data may degrade flavor pairing inferences. Chemical data on flavor compounds is incomplete due to the nature of the experiments that must be conducted to obtain it. Recipe data may have issues due to text parsing errors, imprecision in textual descriptions of ingredients, and the fact that the same ingredient may be known by different names in different recipes. Moreover, the process of matching ingredients in chemical data and recipe data may be fraught with mistakes. Much of the `dirtiness' of the data cannot be cleansed even with manual curation. In this work, we collect a new data set of recipes from Medieval Europe before the Columbian Exchange and investigate the flavor pairing hypothesis historically. To investigate the role of data incompleteness and error as part of this hypothesis testing, we use two separate chemical compound data sets with different levels of cleanliness. Notably, the different data sets give conflicting conclusions about the flavor pairing hypothesis in Medieval Europe. As a contribution towards social science, we obtain inferences about the evolution of culinary arts when many new ingredients are suddenly made available.

physics.soc-ph↗

arXiv2013

Snowmass 2013 Young Physicists Science and Career Survey Report

From April to July 2013 the Snowmass Young Physicists (SYP) administered an online survey collecting the opinions and concerns of the High Energy Physics (HEP) community. The aim of this survey is to provide input into the long term planning meeting known as the Community Summer Study (CSS), or Snowmass on the Mississippi. In total, 1112 respondents took part in the survey including 74 people who had received their training within HEP and have since left for non-academic jobs. This paper presents a summary of the survey results including demographic, career outlook, planned experiments and non-academic career path information collected.

physics.soc-ph↗

arXiv2013

Temporal evolution of adherents of the major religions in Mexico: Avrami model application-Kolgomorov solid training

It applies a mathematical model of solid formation, the model of Avrami-Kolgomorov [Ausloos & Petroni, 2007] to model the time evolution of percentage of adherents of the major religions practiced in Mexico, adjusting the corresponding parameters with available records in the period from 1950 to 2000 [Molina-Hernandez, 2003; INEGI, 2005]. A comparison is made with the application of the model to global trends and concludes that Catholicism is in a marked disaggregation and trends of Christianity in Mexico are similar to global.

physics.soc-ph↗

arXiv2013

Data-driven modeling of systemic delay propagation under severe meteorological conditions

The upsetting consequences of weather conditions are well known to any person involved in air transportation. Still the quantification of how these disturbances affect delay propagation and the effectiveness of managers and pilots interventions to prevent possible large-scale system failures needs further attention. In this work, we employ an agent-based data-driven model developed using real flight performance registers for the entire US airport network and focus on the events occurring on October 27 2010 in the United States. A major storm complex that was later called the 2010 Superstorm took place that day. Our model correctly reproduces the evolution of the delay-spreading dynamics. By considering different intervention measures, we can even improve the model predictions getting closer to the real delay data. Our model can thus be of help to managers as a tool to assess different intervention measures in order to diminish the impact of disruptive conditions in the air transport system.

physics.soc-ph↗

arXiv2013

Power Laws and Fragility in Flow Networks

What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.

physics.soc-ph↗

arXiv2013

Latent Networks Fusion based Model for Event Recommendation in Offline Ephemeral Social Networks

With the growing amount of mobile social media, offline ephemeral social networks (OffESNs) are receiving more and more attentions. Offline ephemeral social networks (OffESNs) are the networks created ad-hoc at a specific location for a specific purpose and lasting for short period of time, relying on mobile social media such as Radio Frequency Identification (RFID) and Bluetooth devices. The primary purpose of people in the OffESNs is to acquire and share information via attending prescheduled events. Event Recommendation over this kind of networks can facilitate attendees on selecting the prescheduled events and organizers on making resource planning. However, because of lack of users preference and rating information, as well as explicit social relations, both rating based traditional recommendation methods and social-trust based recommendation methods can no longer work well to recommend events in the OffESNs. To address the challenges such as how to derive users latent preferences and social relations and how to fuse the latent information in a unified model, we first construct two heterogeneous interaction social networks, an event participation network and a physical proximity network. Then, we use them to derive users latent preferences and latent networks on social relations, including like-minded peers, co-attendees and friends. Finally, we propose an LNF (Latent Networks Fusion) model under a pairwise factor graph to infer event attendance probabilities for recommendation. Experiments on an RFID-based real conference dataset have demonstrated the effectiveness of the proposed model compared with typical solutions.

cs.SI↗

arXiv2013

COINs change leaders - Lessons Learned from a Distributed Course

In this paper we analyze the communication network of 50 students from five universities in three countries participating in a joint course on Collaborative Innovation Networks (COINs). Students formed ten teams. Interaction variables calculated from the e-mail archive of individual team members predict the level of creativity of the team. Oscillating leadership, where members switch between central and peripheral roles is the best predictor of creativity, it is complemented by the variance in the amount of sending or receiving information, and by answering quickly, and positive language. We verify our automatically generated creativity metrics with interviews.

cs.CY↗

arXiv2013

Increasing Knowledge Worker Efficiency through a "Virtual Mirror" of the Social Network

In this paper we introduce a case study describing the combination of manual survey-based and e-mail-based social network analysis. The goal of the project was to increase collaboration efficiency in a team of consultants of a major high tech manufacturer. By analyzing the social network of a team of 42 consultants and comparing it with their utilization as the dependent variable, their efficiency in working together was improved in various way by bridging structure holes and eliminating bottlenecks, reducing stress for overburdened individuals, connecting isolated individuals and identifying the best network structures for high utilization and increased job satisfaction.

cs.SI↗

arXiv2013

Predicting Client Satisfaction through (E-Mail) Network Analysis: The Communication Score Card

This study seeks to better understand the network characteristics of client support teams by analyzing the teams' e-mail communication networks and comparing it to client organization's satisfaction. In collaboration with a large service provider we studied the impact of network properties on the satisfaction of client organizations. In particular, we found that social network metrics correlate with client satisfaction as measured by Net Promoter Score (NPS). A Communication Score Card is suggested as a dashboard to continuously measure client satisfaction, illustrating that data-driven analysis might help improving service providers' service quality management.

cs.SI↗

arXiv2013

Role and Discipline Relationships in a Transdisciplinary Biomedical Team: Structuration, Values Override and Context Scaffolding

Though accepted that "team science" is needed to tackle and conquer the health problems that are plaguing our society significant empirical evidence of team mechanisms and functional dynamics is still lacking in abundance. Through grounded methods the relationship between scientific disciplines and team roles was observed in a United States National Institutes of Health-funded (NIH) research consortium. Interviews and the Organizational Culture Assessment Instrument (OCAI) were employed.. Findings show strong role and discipline idiosyncrasies that when viewed separately provide different insights into team functioning and change receptivity. When considered simultaneously, value-latent characteristics emerged showing self-perceived contributions to the team. This micro/meso analysis suggests that individual participation in team level interactions can inform the structuration of roles and disciplines in an attempt to tackle macro level problems.

cs.CY↗

arXiv2013

The Emergence of Political Discourse on Digital Networks: The Case of the Occupy Movement

How does political discourse spread in digital networks? Can we empirically test if certain conceptual frames of social movements have a correlate on their online discussion networks? Through an analysis of the Twitter data from the Occupy movement, this paper describes the formation of political discourse over time. Building on a previous set of concepts - derived from theoretical discussions about the movement and its roots - we analyse the data to observe when those concepts start to appear within the networks, who are those Twitter users responsible for them, and what are the patterns through which those concepts spread. Preliminary evidence shows that, although there are some signs of opportunistic behaviour among activists, most of them are central nodes from the onset of the network, and shape the discussions across time. These central activists do not only start the conversations around given frames, but also sustain over time and become key members of the network. From here, we aim to provide a thorough account of the "travel" of political discourse, and the correlate of online conversational networks with theoretical accounts of the movement.

cs.SI↗

arXiv2013

The Networks Are Out There: Building Cultural and Economic Resilience Through Informal Communities of Practice

This paper explores the possibilities offered by informal communities of practice to operate as "prototyping spaces" for innovation in the context of developing economies. It begins by looking at the concept of "economic complexity" and how it is useful in both guiding the priorities and evaluating the challenges developing economies face when attempting to drive growth and build measures of resilience, and raises the question of how these economies can both introduce higher complexity activities while compensating for latecomer disadvantages versus more complex economies. It then examines in detail the case of the Twin Eagles Group, a Peruvian video game development community in the 1990s, and how they reverse engineered technologies and global practices to pursue their creative objectives. Based on this case, it concludes by laying out some of the methodological challenges associated to researching this sort of community because of the multi-sited nature of their activities.

cs.SI↗

arXiv2013

Dynamics of Human Social Networks: People, Time, Relationships, and Places

The availability of advanced social interaction sensing technologies provides fine grained data for social network analysis. Although traditional methods of gathering social network data may be subject to human ability to recall social details, people's rating of their closeness to persons in their network is important. This study assesses the relationship amidst closeness ratings, sensed interactions and shared places of recreation. The study found that people tend to give high closeness ratings to people with whom they spend more time. Shared places are correlated to social closeness ratings but not to length of interactions. The results of this study highlight the importance of sensed interactions and closeness ratings.

cs.SI↗

arXiv2013

The Wisdom of Citing Scientists

This Brief Communication discusses the benefits of citation analysis in research evaluation based on Galton's "Wisdom of Crowds" (1907). Citations are based on the assessment of many which is why they can be ascribed a certain amount of accuracy. However, we show that citations are incomplete assessments and that one cannot assume that a high number of citations correlate with a high level of usefulness. Only when one knows that a rarely cited paper has been widely read is it possible to say (strictly speaking) that it was obviously of little use for further research. Using a comparison with 'like' data, we try to determine that cited reference analysis allows a more meaningful analysis of bibliometric data than times-cited analysis.

cs.DL↗

arXiv2013

Comparing the usage of global and local Wikipedias with focus on Swedish Wikipedia

This report summarizes the results of a short-term student research project focused on the usage of Swedish Wikipedia. It is trying to answer the following question: To what extent (and why) do people from non-English language communities use the English Wikipedia instead of the one in their local language? Article access time series and article edit time series from major Wikipedias including Swedish Wikipedia are analyzed with various tools.

physics.soc-ph↗

arXiv2013

PANAS-t: A Pychometric Scale for Measuring Sentiments on Twitter

Online social networks have become a major communication platform, where people share their thoughts and opinions about any topic real-time. The short text updates people post in these network contain emotions and moods, which when measured collectively can unveil the public mood at population level and have exciting implications for businesses, governments, and societies. Therefore, there is an urgent need for developing solid methods for accurately measuring moods from large-scale social media data. In this paper, we propose PANAS-t, which measures sentiments from short text updates in Twitter based on a well-established psychometric scale, PANAS (Positive and Negative Affect Schedule). We test the efficacy of PANAS-t over 10 real notable events drawn from 1.8 billion tweets and demonstrate that it can efficiently capture the expected sentiments of a wide variety of issues spanning tragedies, technology releases, political debates, and healthcare.

cs.SI↗

arXiv2013

Calculation of the Autocorrelation Function of the Stochastic Single Machine Infinite Bus System

Critical slowing down (CSD) is the phenomenon in which a system recovers more slowly from small perturbations. CSD, as evidenced by increasing signal variance and autocorrelation, has been observed in many dynamical systems approaching a critical transition, and thus can be a useful signal of proximity to transition. In this paper, we derive autocorrelation functions for the state variables of a stochastic single machine infinite bus system (SMIB). The results show that both autocorrelation and variance increase as this system approaches a saddle-node bifurcation. The autocorrelation functions help to explain why CSD can be used as an indicator of proximity to criticality in power systems revealing, for example, how nonlinearity in the SMIB system causes these signs to appear.

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