matus medo | University of Fribourg (original) (raw)
Papers by matus medo
Journal of Computational Social Science
Online news can quickly reach and affect millions of people, yet we do not know yet whether there... more Online news can quickly reach and affect millions of people, yet we do not know yet whether there exist potential dynamical regularities that govern their impact on the public. We use data from two major news outlets, BBC and New York Times, where the number of user comments can be used as a proxy of news impact. We find that the impact dynamics of online news articles does not exhibit popularity patterns found in many other social and information systems. In particular, we find that a simple exponential distribution yields a better fit to the empirical news impact distributions than a power-law distribution. This observation is explained by the lack or limited influence of the otherwise omnipresent rich-get-richer mechanism in the analyzed data. The temporal dynamics of the news impact exhibits a universal exponential decay which allows us to collapse individual news trajectories into an elementary single curve. We also show how daily variations of user activity directly influence ...
Communications Physics
How does the complexity of the world around us affect the reliability of our opinions? Motivated ... more How does the complexity of the world around us affect the reliability of our opinions? Motivated by this question, we quantitatively study an opinion formation mechanism whereby an uninformed observer gradually forms opinions about a world composed of subjects interrelated by a signed network of mutual trust and distrust. We show numerically and analytically that the observer’s resulting opinions are highly inconsistent (they tend to be independent of the observer’s initial opinions) and unstable (they exhibit wide stochastic variations). Opinion inconsistency and instability increase with the world’s complexity, intended as the number of subjects and their interactions. This increase can be prevented by suitably expanding the observer’s initial amount of information. Our findings imply that an individual who initially trusts a few credible information sources may end up trusting the deceptive ones even if only a small number of trust relations exist between the credible and decepti...
Physical Review E
Complex networks are often used to represent systems that are not static but grow with time: Peop... more Complex networks are often used to represent systems that are not static but grow with time: People make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology-a time-respecting null model-that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two data sets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.
Entropy
Real networks typically studied in various research fields—ecology and economic complexity, for e... more Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis.
PloS one, 2017
Methods used in information filtering and recommendation often rely on quantifying the similarity... more Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.
The ever-increasing quantity and complexity of scientific production have made it difficult for r... more The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here a method to simultaneously compute reputation of users and quality of scientific artifacts in an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the method is extended by considering author credit, its performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher hhh-index than top papers and top authors chosen by other algorithms.
Yi-Cheng Zhang (directeur de thèse) et Prof. Dionys Baeriswyl (président du jury).
Aggregated data in real world recommender applications often feature fat-tailed distributions of ... more Aggregated data in real world recommender applications often feature fat-tailed distributions of the number of times individual items have been rated or favored. We propose a model to simulate such data. The model is mainly based on social interactions and opinion formation taking place on a complex network with a given topology. A threshold mechanism is used to govern the decision making process that determines whether a user is or is not interested in an item. We demonstrate the validity of the model by fitting attendance distributions from different real data sets. The model is mathematically analyzed by investigating its master equation. Our approach provides an attempt to understand recommender system's data as a social process. The model can serve as a starting point to generate artificial data sets useful for testing and evaluating recommender systems.
Physica A: Statistical Mechanics and its Applications, 2015
Predicting the future evolution of complex systems is one of the main challenges in complexity sc... more Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.
The ever-increasing quantity and complexity of scientific production have made it difficult for r... more The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, and the fact that researchers are keen on promoting their work, has contributed to increasing popularity of online scientific communities. We propose here a reputation algorithm for users of such a community which simultaneously computes user reputation and item quality. The algorithm is evaluated on artificially-generated data as well as on real data from the Econophysics Forum community to determine its best-performing variants. We show that when the algorithm is extended by considering author credit, its performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher hhh-index than top papers and top authors chosen by other algorithms.
The information in this document is subject to change without notice. Company or product names me... more The information in this document is subject to change without notice. Company or product names mentioned in this document may be trademarks or registered trademarks of their respective companies.
Computing Research Repository, 2010
How to rank web pages, scientists and online resources has recently attracted increasing attentio... more How to rank web pages, scientists and online resources has recently attracted increasing attention from both physicists and computer scientists. In this paper, we study the ranking problem of rating systems where users vote objects by discrete ratings. We propose an algorithm that can simultaneously evaluate the user reputation and object quality in an iterative refinement way. According to both
ieeexplore.ieee.org
Nigel Gilbert, University of Surrey, UK Tamas Vinko, Delft University of Technology, The Netherla... more Nigel Gilbert, University of Surrey, UK Tamas Vinko, Delft University of Technology, The Netherlands Mark Jelasity, Hungarian Academy of Science and University of Szeged, Hungary ... Fred Amblard, Université Toulouse 1 Capitole, France Nazareno Andrade, Delft University of Technology, The Netherlands Alastair Gill, University of Surrey, UK David Hales, Delft University of Technology, The Netherlands Dirk Helbing, ETH Zurich, Switzerland Sergi Lozano, ETH Zurich, Switzerland Matus Medo, University of Fribourg, Switzerland Andrzej Nowak, ...
arXiv preprint arXiv:0803.1364, Mar 11, 2008
In the Kelly game (Kelly, 1956), a gambler is allowed to invest a part of the wealth in each turn... more In the Kelly game (Kelly, 1956), a gambler is allowed to invest a part of the wealth in each turn. With a certain probability this investment is doubled, and otherwise it is lost. Motivated by the complexity of real investments, we propose several modifications of this game to investigate the influence of diversification and limited information on investment performance. Analytical and numerical results obtained from these toy games are well related to their real-life counterparts.
arXiv preprint arXiv:0707.0540, Jul 4, 2007
Abstract: The one-mode projecting is extensively used to compress the bipartite networks. Since t... more Abstract: The one-mode projecting is extensively used to compress the bipartite networks. Since the one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method, which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as ...
Journal of Computational Social Science
Online news can quickly reach and affect millions of people, yet we do not know yet whether there... more Online news can quickly reach and affect millions of people, yet we do not know yet whether there exist potential dynamical regularities that govern their impact on the public. We use data from two major news outlets, BBC and New York Times, where the number of user comments can be used as a proxy of news impact. We find that the impact dynamics of online news articles does not exhibit popularity patterns found in many other social and information systems. In particular, we find that a simple exponential distribution yields a better fit to the empirical news impact distributions than a power-law distribution. This observation is explained by the lack or limited influence of the otherwise omnipresent rich-get-richer mechanism in the analyzed data. The temporal dynamics of the news impact exhibits a universal exponential decay which allows us to collapse individual news trajectories into an elementary single curve. We also show how daily variations of user activity directly influence ...
Communications Physics
How does the complexity of the world around us affect the reliability of our opinions? Motivated ... more How does the complexity of the world around us affect the reliability of our opinions? Motivated by this question, we quantitatively study an opinion formation mechanism whereby an uninformed observer gradually forms opinions about a world composed of subjects interrelated by a signed network of mutual trust and distrust. We show numerically and analytically that the observer’s resulting opinions are highly inconsistent (they tend to be independent of the observer’s initial opinions) and unstable (they exhibit wide stochastic variations). Opinion inconsistency and instability increase with the world’s complexity, intended as the number of subjects and their interactions. This increase can be prevented by suitably expanding the observer’s initial amount of information. Our findings imply that an individual who initially trusts a few credible information sources may end up trusting the deceptive ones even if only a small number of trust relations exist between the credible and decepti...
Physical Review E
Complex networks are often used to represent systems that are not static but grow with time: Peop... more Complex networks are often used to represent systems that are not static but grow with time: People make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology-a time-respecting null model-that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two data sets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.
Entropy
Real networks typically studied in various research fields—ecology and economic complexity, for e... more Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis.
PloS one, 2017
Methods used in information filtering and recommendation often rely on quantifying the similarity... more Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.
The ever-increasing quantity and complexity of scientific production have made it difficult for r... more The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here a method to simultaneously compute reputation of users and quality of scientific artifacts in an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the method is extended by considering author credit, its performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher hhh-index than top papers and top authors chosen by other algorithms.
Yi-Cheng Zhang (directeur de thèse) et Prof. Dionys Baeriswyl (président du jury).
Aggregated data in real world recommender applications often feature fat-tailed distributions of ... more Aggregated data in real world recommender applications often feature fat-tailed distributions of the number of times individual items have been rated or favored. We propose a model to simulate such data. The model is mainly based on social interactions and opinion formation taking place on a complex network with a given topology. A threshold mechanism is used to govern the decision making process that determines whether a user is or is not interested in an item. We demonstrate the validity of the model by fitting attendance distributions from different real data sets. The model is mathematically analyzed by investigating its master equation. Our approach provides an attempt to understand recommender system's data as a social process. The model can serve as a starting point to generate artificial data sets useful for testing and evaluating recommender systems.
Physica A: Statistical Mechanics and its Applications, 2015
Predicting the future evolution of complex systems is one of the main challenges in complexity sc... more Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.
The ever-increasing quantity and complexity of scientific production have made it difficult for r... more The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, and the fact that researchers are keen on promoting their work, has contributed to increasing popularity of online scientific communities. We propose here a reputation algorithm for users of such a community which simultaneously computes user reputation and item quality. The algorithm is evaluated on artificially-generated data as well as on real data from the Econophysics Forum community to determine its best-performing variants. We show that when the algorithm is extended by considering author credit, its performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher hhh-index than top papers and top authors chosen by other algorithms.
The information in this document is subject to change without notice. Company or product names me... more The information in this document is subject to change without notice. Company or product names mentioned in this document may be trademarks or registered trademarks of their respective companies.
Computing Research Repository, 2010
How to rank web pages, scientists and online resources has recently attracted increasing attentio... more How to rank web pages, scientists and online resources has recently attracted increasing attention from both physicists and computer scientists. In this paper, we study the ranking problem of rating systems where users vote objects by discrete ratings. We propose an algorithm that can simultaneously evaluate the user reputation and object quality in an iterative refinement way. According to both
ieeexplore.ieee.org
Nigel Gilbert, University of Surrey, UK Tamas Vinko, Delft University of Technology, The Netherla... more Nigel Gilbert, University of Surrey, UK Tamas Vinko, Delft University of Technology, The Netherlands Mark Jelasity, Hungarian Academy of Science and University of Szeged, Hungary ... Fred Amblard, Université Toulouse 1 Capitole, France Nazareno Andrade, Delft University of Technology, The Netherlands Alastair Gill, University of Surrey, UK David Hales, Delft University of Technology, The Netherlands Dirk Helbing, ETH Zurich, Switzerland Sergi Lozano, ETH Zurich, Switzerland Matus Medo, University of Fribourg, Switzerland Andrzej Nowak, ...
arXiv preprint arXiv:0803.1364, Mar 11, 2008
In the Kelly game (Kelly, 1956), a gambler is allowed to invest a part of the wealth in each turn... more In the Kelly game (Kelly, 1956), a gambler is allowed to invest a part of the wealth in each turn. With a certain probability this investment is doubled, and otherwise it is lost. Motivated by the complexity of real investments, we propose several modifications of this game to investigate the influence of diversification and limited information on investment performance. Analytical and numerical results obtained from these toy games are well related to their real-life counterparts.
arXiv preprint arXiv:0707.0540, Jul 4, 2007
Abstract: The one-mode projecting is extensively used to compress the bipartite networks. Since t... more Abstract: The one-mode projecting is extensively used to compress the bipartite networks. Since the one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method, which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as ...