The Web as an Adaptive Network: Coevolution of Web Behavior and Web Structure

Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or state-topology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Scienc

Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User Interfaces 201

Emergence Antecedents of Enterprise Social Media Networks: A Literature Review and Directions for Future Research

What drives the emergence of enterprise social media (ESM) networks? This question cannot be fully answered without studying the scattered body of knowledge. The current research in progress paper addresses this question by means of a preliminary literature review. Precisely, it synthesizes 34 literature findings into a preliminary literature review, which will be further refined and augmented by a research agenda in the future steps. The main theoretical contribution of this paper is to describe 21 antecedents that drive ESM network emergence. In practice, knowledge about these emergence antecedents can be used for various application cases. Examples include developing ESM recommender systems, creating ESM network simulation models, and planning and conducting organizational interventions to optimize ESM networks

An Adaptive Hybrid Method for Link Prediction in Multi-Modal Directed Complex Networks Using the Graph Traversal Pattern

The paper examines the link prediction problem for directed multi-modal complex networks. Specically, a hybrid method combining collaborative filtering and Triadic Closeness methods is developed. The methods are applied to a sample of the GitHub network. Implementation details are discussed, with a focus on design of a scalable system for handilng large data sets. Finally, results of this new method are discussed with no significant improvement over current methods

The asymmetric diffusion of trust between communities: simulations in dynamic social networks

In this work, we present a model of social network showing non-trivial effects on the dynamics of trust and communication. Our model's results meet the characteristics of a typical social network, such as the limited node degree, assortativeness, clustering and communities formation. Simulations have been run first to present some of the most fundamental relations among the main model's attributes. Next, we focused on the emerging asymmetry with which trust develops within different communities in a network. In particular, we considered categories of nodes differing for their communication profiles and a specific example of bridge between two communities. The results are discussed to provide insights about the dynamic formation of communities based on trust relations. These results are the basis for future works with the aim of better understanding the dynamics of the diffusion of trust and its influence on a growing social network