Florian Geigl

Activity Dynamics in Collaboration Networks

Many online collaboration networks struggle to gain user activity and become self-sustaining due to the ramp-up problem or dwindling activity within the system. Prominent examples include online encyclopedias such as (Semantic) MediaWikis, Question and Answering portals such as StackOverflow, and many others. Only a small fraction of these systems manage to reach self-sustaining activity, a level of activity that prevents the system from reverting to a nonactive state. In this article, we model and analyze activity dynamics in synthetic and empirical collaboration networks. Our approach is based on two opposing and well-studied principles: (i) without incentives, users tend to lose interest to contribute and thus, systems become inactive, and (ii) people are susceptible to actions taken by their peers (social or peer influence). With the activity dynamics model that we introduce in this article we can represent typical situations of such collaboration networks. For example, activity in a collaborative network, without external impulses or investments, will vanish over time, eventually rendering the system inactive. However, by appropriately manipulating the activity dynamics and/or the underlying collaboration networks, we can jump-start a previously inactive system and advance it toward an active state. To be able to do so, we first describe our model and its underlying mechanisms. We then provide illustrative examples of empirical datasets and characterize the barrier that has to be breached by a system before it can become self-sustaining in terms of critical mass and activity dynamics. Additionally, we expand on this empirical illustration and introduce a new metric p—the Activity Momentum—to assess the activity robustness of collaboration networks.

Improving recommender system navigability through diversification: a case study of IMDb

The Internet Movie Database (IMDb) is the worlds largest collection of facts about movies and features large-scale recommendation systems connecting hundreds of thousands of items. In the past, the principal evaluation criterion for such recommender systems has been the rating accuracy prediction for recommendations within the immediate one-hop-neighborhood. Apart from a few isolated studies, the evaluation methodology for recommender systems has so far lacked approaches that quantify and measure the exposure to novel content while navigating a recommender system. As such, little is known about the support for navigation and browsing as methods to explore, browse and discover novel items within these systems. In this article, we study the navigability of IMDbs recommender systems over multiple hops. To this end, we analyze the recommendation networks of IMDb with a two-level approach: First, we study reachability in terms of components, path lengths and a bow-tie analysis. Second, we simulate practical browsing scenarios based on greedy decentralized search. Our results show that the IMDb recommendation networks are not very well-suited for navigation scenarios. To mitigate this, we apply a method for diversifying recommendations by specifically selecting recommendations which improve connectivity but do not compromise relevance. We demonstrate that this leads to improved reachability and navigability in both recommender systems. Our work underlines the importance of navigability and reachability as evaluation dimension of a large movie recommender system and shows up ways to increase navigational diversity.

Assessing the Navigational Effects of Click Biases and Link Insertion on the Web

Websites have an inherent interest in steering user navigation in order to, for example, increase sales of specific products or categories, or to guide users towards specific information. In general, website administrators can use the following two strategies to influence their visitors navigation behavior. First, they can introduce click biases to reinforce specific links on their website by changing their visual appearance, for example, by locating them on the top of the page. Second, they can utilize link insertion to generate new paths for users to navigate over. In this paper, we present a novel approach for measuring the potential effects of these two strategies on user navigation. Our results suggest that, depending on the pages for which we want to increase user visits, optimal link modification strategies vary. Moreover, simple topological measures can be used as proxies for assessing the impact of the intended changes on the navigation of users, even before these changes are implemented.

Random surfers on a web encyclopedia

The random surfer model is a frequently used model for simulating user navigation behavior on the Web. Various algorithms, such as PageRank, are based on the assumption that the model represents a good approximation of users browsing a website. However, the way users browse the Web has been drastically altered over the last decade due to the rise of search engines. Hence, new adaptations for the established random surfer model might be required, which better capture and simulate this change in navigation behavior. In this article we compare the classical uniform random surfer to empirical navigation and page access data in a Web Encyclopedia. Our high level contributions are (i) a comparison of stationary distributions of different types of the random surfer to quantify the similarities and differences between those models as well as (ii) new insights into the impact of search engines on traditional user navigation. Our results suggest that the behavior of the random surfer is almost similar to those of users---as long as users do not use search engines. We also find that classical website navigation structures, such as navigation hierarchies or breadcrumbs, only exercise limited influence on user navigation anymore. Rather, a new kind of navigational tools (e.g., recommendation systems) might be needed to better reflect the changes in browsing behavior of existing users.

The influence of social status and network structure on consensus building in collaboration networks

In this paper, we analyze the influence of social status on opinion dynamics and consensus building in collaboration networks. To that end, we simulate the diffusion of opinions in empirical networks and take into account both the network structure and the individual differences of people reflected through their social status. For our simulations, we adapt a well-known Naming Game model and extend it with the Probabilistic Meeting Rule to account for the social status of individuals participating in a meeting. This mechanism is sufficiently flexible and allows us to model various society forms in collaboration networks, as well as the emergence or disappearance of social classes. In particular, we are interested in the way how these society forms facilitate opinion diffusion. Our experimental findings reveal that (i) opinion dynamics in collaboration networks is indeed affected by the individuals’ social status and (ii) this effect is intricate and non-obvious. Our results suggest that in most of the networks the social status favors consensus building. However, relying on it too strongly can also slow down the opinion diffusion, indicating that there is a specific setting for an optimal benefit of social status on the consensus building. On the other hand, in networks where status does not correlate with degree or in networks with a positive degree assortativity consensus is always reached quickly regardless of the status.

The Influence of Social Status on Consensus Building in Collaboration Networks

In this paper, we analyze the influence of social status on opinion dynamics and consensus building in collaboration networks. To that end, we simulate the diffusion of opinions in empirical collaboration networks by taking into account both the network structure and the individual differences of people reflected through their social status. For our simulations, we adapt a well-known Naming Game model and extend it with the Probabilistic Meeting Rule to account for the social status of individuals participating in a meeting. This mechanism is sufficiently flexible and allows us to model various situations in collaboration networks, such as the emergence or disappearance of social classes. In this work, we concentrate on studying three well-known forms of class society: egalitarian, ranked and stratified. In particular, we are interested in the way these society forms facilitate opinion diffusion. Our experimental findings reveal that (i) opinion dynamics in collaboration networks is indeed affected by the individuals social status and (ii) this effect is intricate and non-obvious. In particular, although the social status favors consensus building, relying on it too strongly can slow down the opinion diffusion, indicating that there is a specific setting for each collaboration network in which social status optimally benefits the consensus building process.

BD2K ERuDIte: the Educational Resource Discovery Index for Data Science

The field of data science has developed over the years to enable the efficient integration and analysis of the increasingly large amounts of data being generated across many domains, ranging from social media, to sensor networks, to scientific experiments. Numerous subfields of biology and medicine, such as genetics, neuroimaging, and mobile health, are witnessing a data explosion that promises to revolutionize biomedical science by yielding novel insights and discoveries. To address the challenges posed by biomedical big data, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative (datascience.nih.gov). An important component of this effort is the training of biomedical researchers. To this end, the NIH has funded the BD2K Training Coordinating Center (TCC). A core activity of the BD2K TCC is to develop a web portal (bigdatau.org) to provide personalized training in data science to biomedical researchers. In this paper, we describe our approach and initial efforts in constructing ERuDIte, the Educational Resource Discovery Index for Data Science, which powers the BD2K TCC web portal. ERuDIte harvests a wealth of resources available online for learning data science, both for beginners and experts, including massive open online courses (MOOCs), videos of tutorials and research talks presented at conferences, textbooks, blog posts, and standalone web pages. Though the potential volume of resources is exciting, these online learning materials are highly heterogeneous in quality, difficulty, format, and topic. As a result, this mix of content makes the field intimidating to enter and difficult to navigate. Moreover, data science is a rapidly evolving field, so there is a constant influx of new materials and concepts. ERuDIte leverages data science techniques to build the data science index. This paper describes how ERuDIte uses data extraction, data integration, machine learning, information retrieval, and natural language processing techniques to automatically collect, integrate, describe and organize existing online resources for learning data science.

Steering the Random Surfer on Directed Webgraphs

Ever since the inception of the Web website administrators have tried to steer user browsing behavior for a variety of reasons. For example, to be able to provide the most relevant information, for offering specific products, or to increase revenue from advertisements. One common approach to steer or bias the browsing behavior of users is to influence the link selection process by, for example, highlighting or repositioning links on a website. In this paper, we present a methodology for (i) expressing such navigational biases based on the random surfer model, and for (ii) measuring the consequences of the implemented biases. By adopting a model-based approach we are able to perform a wide range of experiments on seven empirical datasets. Our analyses allows us to gain novel insights into the consequences of navigational biases. Further, we unveil that navigational biases may have significant effects on the browsing processes of users and their typical whereabouts on a website. The first contribution of our work is the formalization of an approach to analyze consequences of navigational biases on the browsing dynamics and visit probabilities of specific pages of a website. Second, we apply this approach to analyze several empirical datasets and improve our understanding of the effects of different biases on real-world websites. In particular, we find that on webgraphs - contrary to undirected networks - typical biases always increase the certainty of the random surfer when selecting a link. Further, we observe significant side effects of biases, which indicate that for practical settings website administrators might need to carefully balance the desired outcomes against undesirable side effects.