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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

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...

Université de Fribourg

Randomizing growing networks with a time-respecting null model

Ren, Zhuo-Ming ; Mariani, Manuel Sebastian ; Zhang, Yi-Cheng ; Medo, Matú?

In: Physical Review E, 2018, vol. 97, no. 5, p. 052311

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...

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

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 of milestone papers through time-balanced network centrality

Mariani, Manuel Sebastian ; Medo, , Matúš ; Zhang, Yi-Cheng

In: Journal of Informetrics, 2016, vol. 10, no. 4, p. 1207–1223

Citations between scientific papers and related bibliometric indices, such as the h- index for authors and the impact factor for journals, are being increasingly used – often in controversial ways – as quantitative tools for research evaluation. Yet, a fundamental research question remains still open: to which extent do quantitative metrics capture the significance of scientific works? We...

Université de Fribourg

The mathematics of non-linear metrics for nested networks

Wu, Rui-Jie ; Shi, Gui-Yuan ; Zhang, Yi-Cheng ; Mariani, Manuel Sebastian

In: Physica A: Statistical Mechanics and its Applications, 2016, vol. 460, p. 254–269

Numerical analysis of data from international trade and ecological networks has shown that the non-linear fitness–complexity metric is the best candidate to rank nodes by importance in bipartite networks that exhibit a nested structure. Despite its relevance for real networks, the mathematical properties of the metric and its variants remain largely unexplored. Here, we perform an analytic...

Université de Fribourg

Ranking nodes in growing networks: When PageRank fails

Mariani, Manuel Sebastian ; Medo, Matúš ; Zhang, Yi-Cheng

In: Scientific Reports, 2015, vol. 5, p. 16181

PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm’s efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank’s...

Université de Fribourg

Calorimetric glass transition in a mean-field theory approach

Mariani, Manuel Sebastian ; Parisi, Giorgio ; Rainone, Corrado

In: Proceedings of the National Academy of Sciences, 2015, vol. 112, no. 8, p. 2361–2366

The study of the properties of glass-forming liquids is difficult for many reasons. Analytic solutions of mean-field models are usually available only for systems embedded in a space with an unphysically high number of spatial dimensions; on the experimental and numerical side, the study of the properties of metastable glassy states requires thermalizing the system in the supercooled liquid...