Structure and Dynamics of Collaborative Information Spaces
This project is related to our research line: Design and analysis of socio-technical systems.
Duration 36 months (May 2015 - April 2018)
Funding source Swiss State Secretariat for Education, Research and Innovation (SERI) (Project C14.0036)
This project is related to the EU COST Action TD1210: "Analyzing the dynamics of information and knowledge landscapes - KNOWeSCAPE"
The convergence of social and technical systems raises a number of important and novel issues. Knowledge spaces like, e.g., the WWW are created, organised and consumed in an increasingly collaborative fashion by groups of humans interacting on short time scales, a process commonly subsumed under the umbrella of social computing or social information processing.
As such, the question how pieces of information are linked to each other, ranked and filtered not only affects the ability of individuals or organisations to access information in a timely, objective and transparent manner. It is also of prime importance for society as a whole since notions of relevance in networks of linked information a) are increasingly influenced by social processes and b) can be an important driver of social dynamics themselves. The resulting feedback between the social and the semantic layer of collaborative knowledge spaces questions to what extent current network-based information ranking measures - even though they are computed by algorithms - can actually be seen as objective. Although the social and the information layer of collaborative knowledge spaces are coupled inseparably, the question how knowledge orders and social dynamics influence each other has been addressed only partially so far. A systems perspective that integrates both layers is still missing.
Figure: In this project, we will explore the possibility to utilise second-order time-aggregated representations (see above) of human navigation behavior for the ranking of information.
In this project, we close this gap by studying social feedback phenomena in information networks from the perspective of multiplex and dynamic networks. By taking a multiplex network perspective, we consider both the social and the information layer of collaborative knowledge spaces, and quantify their properties as well as mutual dependence. Complementary, by employing our competence in temporal network theory, we quantify the navigation behavior of users within information spaces from a complex networks perspective. We study how the retrievability of content and its ranking, in terms of predominant measures of relevance, is influenced by the structure and dynamics of the social systems that create it. We consider this question to be of particular interest and significance due to the recent trend towards "socially influenced" information retrieval systems, like e.g. social search or collaborative filtering techniques. We further address the question how increasingly accessible data on the social dimension of collaborative knowledge spaces can be used to augment existing relevance ranking mechanisms.
Selected publications
Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks
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[2014]
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Scholtes, Ingo;
Wider, Nicolas;
Pfitzner, Rene;
Garas, Antonios;
Tessone, Claudio Juan;
Schweitzer, Frank
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Nature Communications,
pages: 5024,
volume: 5
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Abstract Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Here we introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian temporal networks. Validating our predictions in six data sets we show that compared with the time-aggregated network, non-Markovian characteristics can lead to both a slow-down or speed-up of diffusion, which can even outweigh the decelerating effect of community structures in the static topology. Thus, non-Markovian properties of temporal networks constitute an important additional dimension of complexity in time-varying complex systems.
Predicting Scientific Success Based on Coauthorship Networks
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[2014]
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Sarigol, Emre;
Pfitzner, Rene;
Scholtes, Ingo;
Garas, Antonios;
Schweitzer, Frank
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EPJ Data Science,
pages: 9,
volume: 3
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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.
The Social Dimension of Information Ranking: A Discussion of Research Challenges and Approaches
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[2014]
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Schweitzer, Frank;
Scholtes, Ingo;
Pfitzner, Rene
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Socioinformatics - The Social Impact of Interactions between Humans and IT
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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.
Betweenness preference: Quantifying correlations in the topological dynamics of temporal networks
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[2013]
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Pfitzner, Rene;
Scholtes, Ingo;
Garas, Antonios;
Tessone, Claudio Juan;
Schweitzer, Frank
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Physical Review Letters,
pages: 198701,
volume: 110,
number: 19
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Abstract Time-evolving interaction patterns studied in different contexts can be well represented bytemporal networks in which nodes are intermittently connected. In this Letter we introducethe notion of betweenness preference in the study of temporal networks. It captures how likelya certain node is to mediate interactions between particular pairs of its neighboring nodes.We argue that betweenness preference is an important correlation to consider in the analysisof temporal network data. In particular, it allows to assess to which extent paths existing intime-aggregated, static representations of temporal networks are actually feasible based onthe underlying sequence of interactions. We argue that betweenness preference correlationsare present in empirical data sets. We further show that neglecting betweenness preferencewill lead to significantly wrong statements about spreading dynamics in temporal networks.
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