Abstract

It is widely recognized that citation counts for papers from different fields cannot be directly compared because different scientific fields adopt different citation practices. Citation counts are also strongly biased by paper age since older papers had more time to attract citations. Various procedures aim at suppressing these biases and give rise to new normalized indicators, such as the relative citation count. We use a large citation dataset from Microsoft Academic Graph and a new statistical framework based on the Mahalanobis distance to show that the rankings by well known indicators, including the relative citation count and Google's PageRank score, are significantly biased by paper field and age. Our statistical framework to assess ranking bias allows us to exactly quantify the contributions of each individual field to the overall bias of a given ranking. We propose a general normalization procedure motivated by the z-score which produces much less biased rankings when applied to citation count and PageRank score.

Abstract

The extraction of relevant knowledge from the increasingly large amount of information available in information repositories is one of the big challenges of our time. Although it is clear that the social and the information layer of collaborative knowledge spaces like the World Wide Web (WWW), scholarly publication databases or Online Social Networks (OSNs) are inherently coupled and thus inseparable, the question how the ranking and retrieval of information is influenced by the structure and dynamics of the social systems that create it has been addressed at most partially. In this talk, we will highlight associated research questions and challenges from an ethical, social and computer science perspective and introduce a multiplex network perspective that integrates both the social and the semantic layer of social information systems.

Abstract

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a machine learning classifier, based only on coauthorship network centrality measures at time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing - challenging the perception of citations as an objective, socially unbiased measure of scientific success.

Abstract

We empirically analyze five online communities: Friendster, Livejournal, Facebook, Orkut, Myspace, to identify causes for the decline of social networks. We define social resilience as the ability of a community to withstand changes. We do not argue about the cause of such changes, but concentrate on their impact. Changes may cause users to leave, which may trigger further leaves of others who lost connection to their friends. This may lead to cascades of users leaving. A social network is said to be resilient if the size of such cascades can be limited. To quantify resilience, we use the k-core analysis, to identify subsets of the network in which all users have at least k friends. These connections generate benefits (b) for each user, which have to outweigh the costs (c) of being a member of the network. If this difference is not positive, users leave. After all cascades, the remaining network is the k-core of the original network determined by the cost-to-benefit c/b ratio. By analysing the cumulative distribution of k-cores we are able to calculate the number of users remaining in each community. This allows us to infer the impact of the c/b ratio on the resilience of these online communities. We find that the different online communities have different k-core distributions. Consequently, similar changes in the c/b ratio have a different impact on the amount of active users. As a case study, we focus on the evolution of Friendster. We identify time periods when new users entering the network observed an insufficient c/b ratio. This measure can be seen as a precursor of the later collapse of the community. Our analysis can be applied to estimate the impact of changes in the user interface, which may temporarily increase the c/b ratio, thus posing a threat for the community to shrink, or even to collapse.

Abstract

The goal of human resource management is to ensure an effective company environment. Crucial for a good corporate culture is a comfortable atmosphere and positive social relationships between the employees. The interactions of the people and groups working in the company define their relationships and are reflected in the company's social network. Projections of such networks are Enterprise Social Networks which are more and more integrated in companies. These social networks can be a powerful tool to analyse the structure of a company and indicate potential problems. This extended abstract poses research questions to identify and quantify mechanisms that have an impact on the social network of a company to ensure resilience. To address these questions we make assumptions based on real-world observations for a subsequent model.

Abstract

Software development depends on many factors, including technical, human and social aspects. Due to the complexity of this dependence, a unifying framework must be defined and for this purpose we adopt the complex networks methodology. We use a data-driven approach based on a large collection of open source software projects extracted from online project development platforms. The preliminary results presented in this article reveal that the network perspective yields key insights into the sustainability of software development.

Abstract

In this paper, we propose a simple randomized protocol for identifying trusted nodes based on personalized trust in large scale distributed networks. The problem of identifying trusted nodes, based on personalized trust, in a large network setting stems from the huge computation and message overhead involved in exhaustively calculating and propagating the trust estimates by the remote nodes. However, in any practical scenario, nodes generally communicate with a small subset of nodes and thus exhaustively estimating the trust of all the nodes can lead to huge resource consumption. In contrast, our mechanism can be tuned to locate a desired subset of trusted nodes, based on the allowable overhead, with respect to a particular user. The mechanism is based on a simple exchange of random walk messages and nodes counting the number of times they are being hit by random walkers of nodes in their neighborhood. Simulation results to analyze the effectiveness of the algorithm show that using the proposed algorithm, nodes identify the top trusted nodes in the network with a very high probability by exploring only around 45% of the total nodes, and in turn generates nearly 90% less overhead as compared to an exhaustive trust estimation mechanism, named TrustWebRank. Finally, we provide a measure of the global trustworthiness of a node; simulation results indicate that the measures generated using our mechanism differ by only around 0.6% as compared to TrustWebRank.

Abstract

Over the recent decade, the Internet has conquered people’s homes and life: they pursue an increasing amount of activities on the World Wide Web and this has fundamentally impacted the lifestyle of society. For example, people use their computers for communication with others, to buy and sell products on-line, to search for information, and to carry out many more tasks. Along this development, so far unknown ways of marketing, trading and information sharing are booming. This situation is made possible by a set of related emerging technologies centred around the Internet – just to mention a few: collaborative work and information sharing environments, peer-to-peer networks, and rating, recommendation, and reputation systems. At the economic level, the impact of these technologies is already very high and it is expected to grow even more in the future. The Internet has become a social network, “linking people, organisations, and knowledge” [2] and it has taken the role of a platform on which people pursue an increasing amount of tasks that they have usually only done in the real-world. An approach looking at these emerging technologies and their effects from a complex systems perspective can, as wewill show in this chapter, be very useful.