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arXiv2014

Like, Comment, Repin: User Interaction on Pinterest

We present the results of a study of the Pinterest activity graph. Pinterest is a relatively new and extremely popular content-based social network. Building on a body of work showing that the hidden network whose edges are actual interactions between users is more informative about social relationships that the follower-following network, we study the activity graph composed of links formed by liking, commenting, and repinning, and show that it is very different from the follow network. We collect data about 14 million pins, 7 million repins, 1.6 million likes, and several hundred thousand users and report interesting results about social activity on Pinterest. In particular, we discover that only 12.3% of a user's followers interact with their pins, and over 70% of activity on each user's boards is done by non-followers, but on average, followers who are active perform twice as many actions as non-followers.

cs.SI↗

arXiv2014

Asymmetrically interacting spreading dynamics on complex layered networks

The spread of disease through a physical-contact network and the spread of information about the disease on a communication network are two intimately related dynamical processes. We investigate the asymmetrical interplay between the two types of spreading dynamics, each occurring on its own layer, by focusing on the two fundamental quantities underlying any spreading process: epidemic threshold and the final infection ratio. We find that an epidemic outbreak on the contact layer can induce an outbreak on the communication layer, and information spreading can effectively raise the epidemic threshold. When structural correlation exists between the two layers, the information threshold remains unchanged but the epidemic threshold can be enhanced, making the contact layer more resilient to epidemic outbreak. We develop a physical theory to understand the intricate interplay between the two types of spreading dynamics.

physics.soc-ph↗

arXiv2014

Easily repairable networks

We introduce a simple class of distribution networks which withstand damage by being repairable instead of redundant. We prove a lower bound for the expected cost of repair, and show that for networks on the square and triangular lattice, this bound is achievable and results in a network with exactly three levels of structural hierarchy. We extend our results to networks subject to repeated attacks, in which the repairs themselves must be repairable. We find that, in exchange for a modest increase in repair cost, such networks are able to withstand any number of attacks.

physics.soc-ph↗

arXiv2014

Predicting Central Topics in a Blog Corpus from a Networks Perspective

In today's content-centric Internet, blogs are becoming increasingly popular and important from a data analysis perspective. According to Wikipedia, there were over 156 million public blogs on the Internet as of February 2011. Blogs are a reflection of our contemporary society. The contents of different blog posts are important from social, psychological, economical and political perspectives. Discovery of important topics in the blogosphere is an area which still needs much exploring. We try to come up with a procedure using probabilistic topic modeling and network centrality measures which identifies the central topics in a blog corpus.

cs.IR↗

arXiv2014

Dreamtime Astronomy: development of a new Indigenous program at Sydney Observatory

The Australian National Curriculum promotes Indigenous culture in school education programs. To foster a broader appreciation of cultural astronomy, to utilise the unique astronomical heritage of the site, and to develop an educational program within the framework of the National Curriculum, Sydney Observatory launched Dreamtime Astronomy, a program incorporating Australian Indigenous culture, astronomy, and Sydney's astronomical history and heritage. This paper reviews the development and implementation of this program and discusses modifications following an evaluation by schools.

physics.ed-ph↗

arXiv2014

Learning modular structures from network data and node variables

A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structure among modules. However, estimation solely from node-specific variables can lead to spurious dependencies, and unverifiable structural assumptions are often used for regularization. Here, we propose an extended model that leverages direct observations about the network in addition to node-specific variables. By integrating complementary data types, we avoid the need for structural assumptions. We illustrate theoretical and practical significance of the model and develop a reversible-jump MCMC learning procedure for learning modules and model parameters. We demonstrate the method accuracy in predicting modular structures from synthetic data and capability to learn influence structures in twitter data and regulatory modules in the Mycobacterium tuberculosis gene regulatory network.

stat.ML↗

arXiv2014

News-Based Group Modeling and Forecasting

In this paper, we study news group modeling and forecasting methods using quantitative data generated by our large-scale natural language processing (NLP) text analysis system. A news group is a set of news entities, like top U.S. cities, governors, senators, golfers, or movie actors. Our fame distribution analysis of news groups shows that log-normal and power-law distributions generally could describe news groups in many aspects. We use several real news groups including cities, politicians, and CS professors, to evaluate our news group models in terms of time series data distribution analysis, group-fame probability analysis, and fame-changing analysis over long time. We also build a practical news generation model using a HMM (Hidden Markov Model) based approach. Most importantly, our analysis shows the future entity fame distribution has a power-law tail. That is, only a small number of news entities in a group could become famous in the future. Based on these analysis we are able to answer some interesting forecasting problems - for example, what is the future average fame (or maximum fame) of a specific news group? And what is the probability that some news entity become very famous within a certain future time range? We also give concrete examples to illustrate our forecasting approaches.

cs.SI↗

arXiv2014

Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm

Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we need? Are there some network structures which are more difficult than others to recover? Can we design efficient inference algorithms with provable guarantees? Despite the increasing availability of cascade data and methods for inferring networks from these data, a thorough theoretical understanding of the above questions remains largely unexplored in the literature. In this paper, we investigate the network structure inference problem for a general family of continuous-time diffusion models using an $l_1$-regularized likelihood maximization framework. We show that, as long as the cascade sampling process satisfies a natural incoherence condition, our framework can recover the correct network structure with high probability if we observe $O(d^3 \log N)$ cascades, where $d$ is the maximum number of parents of a node and $N$ is the total number of nodes. Moreover, we develop a simple and efficient soft-thresholding inference algorithm, which we use to illustrate the consequences of our theoretical results, and show that our framework outperforms other alternatives in practice.

cs.SI↗

arXiv2014

Sampling node group structure of social and information networks

Lately, network sampling proved as a promising tool for simplifying large real-world networks and thus providing for their faster and more efficient analysis. Still, understanding the changes of network structure and properties under different sampling methods remains incomplete. In this paper, we analyze the presence of characteristic group of nodes (i.e., communities, modules and mixtures of the two) in social and information networks. Moreover, we observe the changes of node group structure under two sampling methods, random node selection based on degree and breadth-first sampling. We show that the sampled information networks contain larger number of mixtures than original networks, while the structure of sampled social networks exhibits stronger characterization by communities. The results also reveal there exist no significant differences in the behavior of both sampling methods. Accordingly, the selection of sampling method impact on the changes of node group structure to a much smaller extent that the type and the structure of analyzed network.

cs.SI↗

arXiv2014

Be In The Know: Connecting News Articles to Relevant Twitter Conversations

In the era of data-driven journalism, data analytics can deliver tools to support journalists in connecting to new and developing news stories, e.g., as echoed in micro-blogs such as Twitter, the new citizen-driven media. In this paper, we propose a framework for tracking and automatically connecting news articles to Twitter conversations as captured by Twitter hashtags. For example, such a system could alert journalists about news that get a lot of Twitter reaction, so that they can investigate those conversations for new developments in the story, promote their article to a set of interested consumers, or discover general sentiment towards the story. Mapping articles to appropriate hashtags is nevertheless very challenging, due to different language styles used in articles versus tweets, the streaming aspect of news and tweets, as well as the user behavior when marking certain tweet-terms as hashtags. As a case-study, we continuously track the RSS feeds of Irish Times news articles and a focused Twitter stream over a two months period, and present a system that assigns hashtags to each article, based on its Twitter echo. We propose a machine learning approach for classifying and ranking article-hashtag pairs. Our empirical study shows that our system delivers high precision for this task.

cs.SI↗

arXiv2014

Locally Boosted Graph Aggregation for Community Detection

Learning the right graph representation from noisy, multi-source data has garnered significant interest in recent years. A central tenet of this problem is relational learning. Here the objective is to incorporate the partial information each data source gives us in a way that captures the true underlying relationships. To address this challenge, we present a general, boosting-inspired framework for combining weak evidence of entity associations into a robust similarity metric. Building on previous work, we explore the extent to which different local quality measurements yield graph representations that are suitable for community detection. We present empirical results on a variety of datasets demonstrating the utility of this framework, especially with respect to real datasets where noise and scale present serious challenges. Finally, we prove a convergence theorem in an ideal setting and outline future research into other application domains.

cs.LG↗

arXiv2014

How to Ask for a Favor: A Case Study on the Success of Altruistic Requests

Requests are at the core of many social media systems such as question & answer sites and online philanthropy communities. While the success of such requests is critical to the success of the community, the factors that lead community members to satisfy a request are largely unknown. Success of a request depends on factors like who is asking, how they are asking, when are they asking, and most critically what is being requested, ranging from small favors to substantial monetary donations. We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors. Drawing from social psychology literature, we extract high-level social features from text that operationalize social relations between recipient and donor and demonstrate that these extracted relations are predictive of success. More specifically, we find that clearly communicating need through the narrative is essential and that that linguistic indications of gratitude, evidentiality, and generalized reciprocity, as well as high status of the asker further increase the likelihood of success. Building on this understanding, we develop a model that can predict the success of unseen requests, significantly improving over several baselines. We link these findings to research in psychology on helping behavior, providing a basis for further analysis of success in social media systems.

cs.CL↗

arXiv2014

Operational risk of a wind farm energy production by Extreme Value Theory and Copulas

In this paper we use risk management techniques to evaluate the potential effects of those operational risks that affect the energy production of a wind farm. We concentrate our attention on three major risk factors: wind speed uncertainty, wind turbine reliability and interactions of wind turbines due mainly to their placement. As a first contribution, we show that the Weibull distribution, commonly used to fit recorded wind speed data, underestimates rare events. Therefore, in order to achieve a better estimation of the tail of the wind speed distribution, we advance a Generalized Pareto distribution. The wind turbines reliability is considered by modeling the failures events as a compound Poisson process. Finally, the use of Copula able us to consider the correlation between wind turbines that compose the wind farm. Once this procedure is set up, we show a sensitivity analysis and we also compare the results from the proposed procedure with those obtained by ignoring the aforementioned risk factors.

physics.soc-ph↗

arXiv2014

Complex Networks Measures for Differentiation between Normal and Shuffled Croatian Texts

This paper studies the properties of the Croatian texts via complex networks. We present network properties of normal and shuffled Croatian texts for different shuffling principles: on the sentence level and on the text level. In both experiments we preserved the vocabulary size, word and sentence frequency distributions. Additionally, in the first shuffling approach we preserved the sentence structure of the text and the number of words per sentence. Obtained results showed that degree rank distributions exhibit no substantial deviation in shuffled networks, and strength rank distributions are preserved due to the same word frequencies. Therefore, standard approach to study the structure of linguistic co-occurrence networks showed no clear difference among the topologies of normal and shuffled texts. Finally, we showed that the in- and out- selectivity values from shuffled texts are constantly below selectivity values calculated from normal texts. Our results corroborate that the node selectivity measure can capture structural differences between original and shuffled Croatian texts.

cs.CL↗

arXiv2014

Mining Urban Performance: Scale-Independent Classification of Cities Based on Individual Economic Transactions

Intensive development of urban systems creates a number of challenges for urban planners and policy makers in order to maintain sustainable growth. Running efficient urban policies requires meaningful urban metrics, which could quantify important urban characteristics including various aspects of an actual human behavior. Since a city size is known to have a major, yet often nonlinear, impact on the human activity, it also becomes important to develop scale-free metrics that capture qualitative city properties, beyond the effects of scale. Recent availability of extensive datasets created by human activity involving digital technologies creates new opportunities in this area. In this paper we propose a novel approach of city scoring and classification based on quantitative scale-free metrics related to economic activity of city residents, as well as domestic and foreign visitors. It is demonstrated on the example of Spain, but the proposed methodology is of a general character. We employ a new source of large-scale ubiquitous data, which consists of anonymized countrywide records of bank card transactions collected by one of the largest Spanish banks. Different aspects of the classification reveal important properties of Spanish cities, which significantly complement the pattern that might be discovered with the official socioeconomic statistics.

physics.soc-ph↗

arXiv2014

Partitioning Networks with Node Attributes by Compressing Information Flow

Real-world networks are often organized as modules or communities of similar nodes that serve as functional units. These networks are also rich in content, with nodes having distinguishing features or attributes. In order to discover a network's modular structure, it is necessary to take into account not only its links but also node attributes. We describe an information-theoretic method that identifies modules by compressing descriptions of information flow on a network. Our formulation introduces node content into the description of information flow, which we then minimize to discover groups of nodes with similar attributes that also tend to trap the flow of information. The method has several advantages: it is conceptually simple and does not require ad-hoc parameters to specify the number of modules or to control the relative contribution of links and node attributes to network structure. We apply the proposed method to partition real-world networks with known community structure. We demonstrate that adding node attributes helps recover the underlying community structure in content-rich networks more effectively than using links alone. In addition, we show that our method is faster and more accurate than alternative state-of-the-art algorithms.

cs.SI↗

arXiv2014

Preliminary Report on the Structure of Croatian Linguistic Co-occurrence Networks

In this article, we investigate the structure of Croatian linguistic co-occurrence networks. We examine the change of network structure properties by systematically varying the co-occurrence window sizes, the corpus sizes and removing stopwords. In a co-occurrence window of size $n$ we establish a link between the current word and $n-1$ subsequent words. The results point out that the increase of the co-occurrence window size is followed by a decrease in diameter, average path shortening and expectedly condensing the average clustering coefficient. The same can be noticed for the removal of the stopwords. Finally, since the size of texts is reflected in the network properties, our results suggest that the corpus influence can be reduced by increasing the co-occurrence window size.

cs.CL↗

arXiv2014

It Doesn't Break Just on Twitter. Characterizing Facebook content During Real World Events

Multiple studies in the past have analyzed the role and dynamics of the Twitter social network during real world events. However, little work has explored the content of other social media services, or compared content across two networks during real world events. We believe that social media platforms like Facebook also play a vital role in disseminating information on the Internet during real world events. In this work, we study and characterize the content posted on the world's biggest social network, Facebook, and present a comparative analysis of Facebook and Twitter content posted during 16 real world events. Contrary to existing notion that Facebook is used mostly as a private network, our findings reveal that more than 30% of public content that was present on Facebook during these events, was also present on Twitter. We then performed qualitative analysis on the content spread by the most active users during these events, and found that over 10% of the most active users on both networks post spam content. We used stylometric features from Facebook posts and tweet text to classify this spam content, and were able to achieve an accuracy of over 99% for Facebook, and over 98% for Twitter. This work is aimed at providing researchers with an overview of Facebook content during real world events, and serve as basis for more in-depth exploration of its various aspects like information quality, and credibility during real world events.

cs.SI↗