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arXiv2013

Evaluating Astronomy Literacy of the General Public

A scientifically literate society is important for many different reasons, some of which include democratic and scientific topics. This study was performed in order to identify topics in astronomy and science in general that may not be well understood by the general public. Approximately 1,000 adults at a popular science museum in Philadelphia, PA completed True-False survey questions about basic astronomy concepts. The participants were also asked to provide their age, gender, and highest degree obtained. Although 93 +/- 0.8% of the participants correctly answered that scientists can calculate the age of the Earth, only 58 +/- 2% provided the correct response that scientists can calculate the age of the Universe. Some participants (30 +/- 1%) responded that scientists have found life on Mars. Females scored an average total score of 78 +/- 2%, whereas males scored an average 85 +/- 1%. Participants with an age of 56 and over had an average score of 78 +/- 4% compared to participants under the age of 56 that were found to have an average score of 82 +/- 2%. Lastly, participants' highest degree obtained scaled with number of correct responses, with graduate level degree earners providing the largest amount of correct responses and an average score of 86 +/- 2%.

physics.soc-ph↗

arXiv2013

What can Social Media teach us about protests? Analyzing the Chilean 2011-12 Student Movement's Network evolution through Twitter data

Using social media data -specially twitter -of the Chilean 2011-12 student movement, we study their social network evolution over time to analyze how leaders and participants self-organize and spread information. Based on a few key events of the student movement's timeline, we visualize the student network trajectory and analyze their structural and semantic properties. Therefore, in this paper we: i) describe the basic network topology of the 2011-12 Chilean massive student movement; ii) explore how the 180 key central nodes of the movement are connected, self-organize and spread information. We contend that this social media enabled massive movement is yet another manifestation of the network era, which leverages agents' socio-technical networks, and thus accelerates how agents coordinate, mobilize resources and enact collective intelligence.

cs.SI↗

arXiv2013

Alpha current flow betweenness centrality

A class of centrality measures called betweenness centralities reflects degree of participation of edges or nodes in communication between different parts of the network. The original shortest-path betweenness centrality is based on counting shortest paths which go through a node or an edge. One of shortcomings of the shortest-path betweenness centrality is that it ignores the paths that might be one or two steps longer than the shortest paths, while the edges on such paths can be important for communication processes in the network. To rectify this shortcoming a current flow betweenness centrality has been proposed. Similarly to the shortest path betwe has prohibitive complexity for large size networks. In the present work we propose two regularizations of the current flow betweenness centrality, α-current flow betweenness and truncated α-current flow betweenness, which can be computed fast and correlate well with the original current flow betweenness.

cs.SI↗

arXiv2013

Stochastic Models Predict User Behavior in Social Media

User response to contributed content in online social media depends on many factors. These include how the site lays out new content, how frequently the user visits the site, how many friends the user follows, how active these friends are, as well as how interesting or useful the content is to the user. We present a stochastic modeling framework that relates a user's behavior to details of the site's user interface and user activity and describe a procedure for estimating model parameters from available data. We apply the model to study discussions of controversial topics on Twitter, specifically, to predict how followers of an advocate for a topic respond to the advocate's posts. We show that a model of user behavior that explicitly accounts for a user transitioning through a series of states before responding to an advocate's post better predicts response than models that fail to take these states into account. We demonstrate other benefits of stochastic models, such as their ability to identify users who are highly interested in advocate's posts.

cs.CY↗

arXiv2013

The Limits of Phenomenology: From Behaviorism to Drug Testing and Engineering Design

It is widely believed that theory is useful in physics because it describes simple systems and that strictly empirical phenomenological approaches are necessary for complex biological and social systems. Here we prove based upon an analysis of the information that can be obtained from experimental observations that theory is even more essential in the understanding of complex systems. Implications of this proof revise the general understanding of how we can understand complex systems including the behaviorist approach to human behavior, problems with testing engineered systems, and medical experimentation for evaluating treatments and the FDA approval of medications. Each of these approaches are inherently limited in their ability to characterize real world systems due to the large number of conditions that can affect their behavior. Models are necessary as they can help to characterize behavior without requiring observations for all possible conditions. The testing of models by empirical observations enhances the utility of those observations. For systems for which adequate models have not been developed, or are not practical, the limitations of empirical testing lead to uncertainty in our knowledge and risks in individual, organizational and social policy decisions. These risks should be recognized and inform our decisions.

physics.soc-ph↗

arXiv2013

Estimating Clique Composition and Size Distributions from Sampled Network Data

Cliques are defined as complete graphs or subgraphs; they are the strongest form of cohesive subgroup, and are of interest in both social science and engineering contexts. In this paper we show how to efficiently estimate the distribution of clique sizes from a probability sample of nodes obtained from a graph (e.g., by independence or link-trace sampling). We introduce two types of unbiased estimators, one of which exploits labeling of sampled nodes neighbors and one of which does not require this information. We compare the estimators on a variety of real-world graphs and provide suggestions for their use. We generalize our estimators to cases in which cliques are distinguished not only by size but also by node attributes, allowing us to estimate clique composition by size. Finally, we apply our methodology to a sample of Facebook users to estimate the clique size distribution by gender over the social graph.

cs.SI↗

arXiv2013

Models of on-line social networks

We present a deterministic model for on-line social networks (OSNs) based on transitivity and local knowledge in social interactions. In the Iterated Local Transitivity (ILT) model, at each time-step and for every existing node $x$, a new node appears which joins to the closed neighbour set of $x.$ The ILT model provably satisfies a number of both local and global properties that were observed in OSNs and other real-world complex networks, such as a densification power law, decreasing average distance, and higher clustering than in random graphs with the same average degree. Experimental studies of social networks demonstrate poor expansion properties as a consequence of the existence of communities with low number of inter-community edges. Bounds on the spectral gap for both the adjacency and normalized Laplacian matrices are proved for graphs arising from the ILT model, indicating such bad expansion properties. The cop and domination number are shown to remain the same as the graph from the initial time-step $G_0$, and the automorphism group of $G_0$ is a subgroup of the automorphism group of graphs generated at all later time-steps. A randomized version of the ILT model is presented, which exhibits a tuneable densification power law exponent, and maintains several properties of the deterministic model.

cs.SI↗

arXiv2013

Random Walks on Directed Networks: Inference and Respondent-driven Sampling

Respondent driven sampling (RDS) is a method often used to estimate population properties (e.g. sexual risk behavior) in hard-to-reach populations. It combines an effective modified snowball sampling methodology with an estimation procedure that yields unbiased population estimates under the assumption that the sampling process behaves like a random walk on the social network of the population. Current RDS estimation methodology assumes that the social network is undirected, i.e. that all edges are reciprocal. However, empirical social networks in general also have non-reciprocated edges. To account for this fact, we develop a new estimation method for RDS in the presence of directed edges on the basis of random walks on directed networks. We distinguish directed and undirected edges and consider the possibility that the random walk returns to its current position in two steps through an undirected edge. We derive estimators of the selection probabilities of individuals as a function of the number of outgoing edges of sampled individuals. We evaluate the performance of the proposed estimators on artificial and empirical networks to show that they generally perform better than existing methods. This is in particular the case when the fraction of directed edges in the network is large.

stat.ME↗

arXiv2013

Traffic Optimization to Control Epidemic Outbreaks in Metapopulation Models

We propose a novel framework to study viral spreading processes in metapopulation models. Large subpopulations (i.e., cities) are connected via metalinks (i.e., roads) according to a metagraph structure (i.e., the traffic infrastructure). The problem of containing the propagation of an epidemic outbreak in a metapopulation model by controlling the traffic between subpopulations is considered. Controlling the spread of an epidemic outbreak can be written as a spectral condition involving the eigenvalues of a matrix that depends on the network structure and the parameters of the model. Based on this spectral condition, we propose a convex optimization framework to find cost-optimal approaches to traffic control in epidemic outbreaks.

physics.soc-ph↗

arXiv2013

Detection and Filtering of Collaborative Malicious Users in Reputation System using Quality Repository Approach

Online reputation system is gaining popularity as it helps a user to be sure about the quality of a product/service he wants to buy. Nonetheless online reputation system is not immune from attack. Dealing with malicious ratings in reputation systems has been recognized as an important but difficult task. This problem is challenging when the number of true user's ratings is relatively small and unfair ratings plays majority in rated values. In this paper, we have proposed a new method to find malicious users in online reputation systems using Quality Repository Approach (QRA). We mainly concentrated on anomaly detection in both rating values and the malicious users. QRA is very efficient to detect malicious user ratings and aggregate true ratings. The proposed reputation system has been evaluated through simulations and it is concluded that the QRA based system significantly reduces the impact of unfair ratings and improve trust on reputation score with lower false positive as compared to other method used for the purpose.

cs.SI↗

arXiv2013

Multifractal portrayal of the Swiss population

Fractal geometry is a fundamental approach for describing the complex irregularities of the spatial structure of point patterns. The present research characterizes the spatial structure of the Swiss population distribution in the three Swiss geographical regions (Alps, Plateau and Jura) and at the entire country level. These analyses were carried out using fractal and multifractal measures for point patterns, which enabled the estimation of the spatial degree of clustering of a distribution at different scales. The Swiss population dataset is presented on a grid of points and thus it can be modelled as a "point process" where each point is characterized by its spatial location (geometrical support) and a number of inhabitants (measured variable). The fractal characterization was performed by means of the box-counting dimension and the multifractal analysis was conducted through the Renyi's generalized dimensions and the multifractal spectrum. Results showed that the four population patterns are all multifractals and present different clustering behaviours. Applying multifractal and fractal methods at different geographical regions and at different scales allowed us to quantify and describe the dissimilarities between the four structures and their underlying processes. This paper is the first Swiss geodemographic study applying multifractal methods using high resolution data.

physics.soc-ph↗

arXiv2013

The S-metric, the Beichl-Cloteaux approximation, and preferential attachment

The S-metric has grown popular in network studies, as a measure of ``scale-freeness'' restricted to the collection G(D) of connected graphs with a common degree sequence D=(d_1,\ldots,d_n). The calculation of S depends on the maximum possible degree assortativity r among graphs in G(D). The original method involves a heuristic construction of a maximally assortative graph g*. The approximation by Beichl and Cloteaux involves constructing a possibly disconnected graph g' with r(g') >= r(g*) and requires O(n^2) tests for the graphicality of a degree sequence. The present paper uses the Tripathi-Vijay test to streamline this approximation, and thereby to investigate two collections of graphs: Barabasi-Albert trees and coauthorship graphs of mathematical sciences researchers. Long-term trends in the coauthorship graphs are discussed, and contextualized by insights derived from the BA trees. It is known that greater degree-based preferential attachment produces greater variance in degree sequences, and these trees exhibited assortativities restricted to a narrow band. In contrast, variance in degree rose over time in the coauthorship graphs in spite of weakening degree-based preferential attachment. These observations and their implications are discussed and avenues of future work are suggested.

math.CO↗

arXiv2013

Time Development of Early Social Networks: Link analysis and group dynamics

Empirical data on early network history are rare. Students beginning their studies at a university with no or few prior connections to each other offer a unique opportunity to investigate the formation and early development of social networks. During a nine week introductory physics course, first year physics students were asked to identify those with whom they communicated about problem solving in physics during the preceding week. We use these students' self reports to produce time dependent student interaction networks. These networks have also been investigated to elucidate possible effects of gender and students' final course grade. Changes in the weekly number of links are investigated to show that while roughly half of all links change from week to week, students also reestablish a growing number of links as they progress through their first weeks of study. To investigate how students group, Infomap is used to establish groups. Further, student group flow is examined using alluvial diagrams, showing that many students jump between group each week., Finally, a segregation measure is developed which shows that students structure themselves according to gender and laboratory exercise groups and not according to end-of-course grade. The results show the behavior of an early social-educational network, and may have implications for theoretical network models as well as for physics education.

physics.soc-ph↗

arXiv2013

Topological security assessment of technological networks

The spreading of dangerous malware or faults in inter-dependent networks of electronics devices has raised deep concern, because from the ICT networks infections may propagate to other Critical Infrastructures producing the well-known domino or cascading effect. Researchers are attempting to develop a high level analysis of malware propagation discarding software details, in order to generalize to the maximum extent the defensive strategies. For example, it has been suggested that the maximum eigenvalue of the network adjacency matrix could act as a threshold for the malware's spreading. This leads naturally to use the spectral graph theory to identify the most critical and influential nodes in technological networks. Many well-known graph parameters have been studied in the past years to accomplish the task. Here we test our AV11 algorithm showing that outperforms degree, closeness, betweenness centrality and the dynamical importance

cs.SI↗

arXiv2013

Incorporating Text Analysis into Evolution of Social Groups in Blogosphere

Data reflecting social and business relations has often form of network of connections between entities (called social network). In such network important and influential users can be identified as well as groups of strongly connected users. Finding such groups and observing their evolution becomes an increasingly important research problem. One of the significant problems is to develop method incorporating not only information about connections between entities but also information obtained from text written by the users. Method presented in this paper combine social network analysis and text mining in order to understand groups evolution.

cs.SI↗

arXiv2013

Sentiment in New York City: A High Resolution Spatial and Temporal View

Measuring public sentiment is a key task for researchers and policymakers alike. The explosion of available social media data allows for a more time-sensitive and geographically specific analysis than ever before. In this paper we analyze data from the micro-blogging site Twitter and generate a sentiment map of New York City. We develop a classifier specifically tuned for 140-character Twitter messages, or tweets, using key words, phrases and emoticons to determine the mood of each tweet. This method, combined with geotagging provided by users, enables us to gauge public sentiment on extremely fine-grained spatial and temporal scales. We find that public mood is generally highest in public parks and lowest at transportation hubs, and locate other areas of strong sentiment such as cemeteries, medical centers, a jail, and a sewage facility. Sentiment progressively improves with proximity to Times Square. Periodic patterns of sentiment fluctuate on both a daily and a weekly scale: more positive tweets are posted on weekends than on weekdays, with a daily peak in sentiment around midnight and a nadir between 9:00 a.m. and noon.

physics.soc-ph↗

arXiv2013

Discovering Latent Patterns from the Analysis of User-Curated Movie Lists

User content curation is becoming an important source of preference data, as well as providing information regarding the items being curated. One popular approach involves the creation of lists. On Twitter, these lists might contain accounts relevant to a particular topic, whereas on a community site such as the Internet Movie Database (IMDb), this might take the form of lists of movies sharing common characteristics. While list curation involves substantial combined effort on the part of users, researchers have rarely looked at mining the outputs of this kind of crowdsourcing activity. Here we study a large collection of movie lists from IMDb. We apply network analysis methods to a graph that reflects the degree to which pairs of movies are "co-listed", that is, assigned to the same lists. This allows us to uncover a more nuanced grouping of movies that goes beyond categorisation schemes based on attributes such as genre or director.

cs.SI↗

arXiv2013

Peer Pressure Shapes Consensus, Leadership, and Innovations in Social Groups

What is the effect of the combined direct and indirect social influences-peer pressure (PP)-on a social groups collective decisions? We present a model that captures PP as a function of the socio-cultural distance between individuals in a social group. Using this model and empirical data from 15 real-world social networks we found that the PP level determines how fast a social group reaches consensus. More importantly, the levels of PP determine the leaders who can achieve full control of their social groups. PP can overcome barriers imposed upon a consensus by the existence of tightly connected communities with local leaders or the existence of leaders with poor cohesiveness of opinions. A moderate level of PP is also necessary to explain the rate at which innovations diffuse through a variety of social groups.

physics.soc-ph↗