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Université de Fribourg

Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data

Xu, Shuqi ; Mariani, Manuel Sebastian ; Lü, Linyuan ; Medo, Matúš

In: Journal of Informetrics, 2020, vol. 14, no. 1, p. 101005

Despite the increasing use of citation-based metrics for research evaluation purposes, we do not know yet which metrics best deliver on their promise to gauge the significance of a scientific paper or a patent. We assess 17 network-based metrics by their ability to identify milestone papers and patents in three large citation datasets. We find that traditional information-retrieval evaluation...

Université de Fribourg

The long-term impact of ranking algorithms in growing networks

Zhang, Shilun ; Medo, Matúš ; Lü, Linyuan ; Mariani, Manuel Sebastian

In: Information Sciences, 2019, vol. 488, p. 257–271

When users search online for content, they are constantly exposed to rankings. For example, web search results are presented as a ranking of relevant websites, and online bookstores often show us lists of best-selling books. While popularity-based ranking algorithms (like Google’s PageRank) have been extensively studied in previous works, we still lack a clear understanding of their...

Université de Fribourg

Discoverers in scientific citation data

Shi, Gui-Yuan ; Kong, Yi-Xiu ; Yuan, Guang-Hui ; Wu, Rui-Jie ; Zeng, An ; Medo, Matúš

In: Journal of Informetrics, 2019, vol. 13, no. 2, p. 717–725

Identifying the future influential papers among the newly published ones is an important yet challenging issue in bibliometrics. As newly published papers have no or limited citation history, linear extrapolation of their citation counts—which is motivated by the well-known preferential attachment mechanism—is not applicable. We translate the recently introduced notion of discoverers to...

Université de Fribourg

Link prediction in bipartite nested networks

Medo, Matúš ; Mariani, Manuel Sebastian ; Lü, Linyuan

In: Entropy, 2018, vol. 20, no. 10, p. 777

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...

Consortium of Swiss Academic Libraries

The effect of the initial network configuration on preferential attachment

Berset, Yves ; Medo, Matúš

In: The European Physical Journal B, 2013, vol. 86, no. 6, p. 1-7

Université de Fribourg

Ranking in evolving complex networks

Liao, Hao ; Mariani, Manuel Sebastian ; Medo, Matúš ; Zhang, Yi-Cheng ; Zhou, Ming-Yang

In: Physics Reports, 2017, vol. 689, p. 1–54

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are...

Université de Fribourg

Information filtering based on corrected redundancy-eliminating mass diffusion

Zhu, Xuzhen ; Yang, Yujie ; Chen, Guilin ; Medo, Matúš ; Tian, Hui ; Cai, Shi-Min

In: PLOS ONE, 2017, vol. 12, no. 7, p. e0181402

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...

Université de Fribourg

Quantifying and suppressing ranking bias in a large citation network

Vaccario, Giacomo ; Medo, Matúš ; Wider, Nicolas ; Mariani, Manuel Sebastian

In: Journal of Informetrics, 2017, vol. 11, no. 3, p. 766–782

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...

Université de Fribourg

Identification and impact of discoverers in online social systems

Medo, Matúš ; Mariani, Manuel S. ; Zeng, An ; Zhang, Yi-Cheng

In: Scientific Reports, 2016, vol. 6, p. 34218

Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by...

Université de Fribourg

Model-based evaluation of scientific impact indicators

Medo, Matúš ; Cimini, Giulio

In: Physical Review E, 2016, vol. 94, no. 3, p. 032312

Using bibliometric data artificially generated through a model of citation dynamics calibrated on empirical data, we compare several indicators for the scientific impact of individual researchers. The use of such a controlled setup has the advantage of avoiding the biases present in real databases, and it allows us to assess which aspects of the model dynamics and which traits of individual...

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