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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...

Consortium of Swiss Academic Libraries

New approaches to multi-objective optimization

Grandoni, Fabrizio ; Ravi, R. ; Singh, Mohit ; Zenklusen, Rico

In: Mathematical Programming, 2014, vol. 146, no. 1-2, p. 525-554

Université de Fribourg

Complexity of two coloring problems in cubic planar bipartite mixed graphs

Ries, Bernard

In: Discrete Applied Mathematics, 2010, vol. 158, no. 5, p. 592-596

In this note we consider two coloring problems in mixed graphs, i.e., graphs containing edges and arcs, which arise from scheduling problems where disjunctive and precedence constraints have to be taken into account. We show that they are both NP-complete in cubic planar bipartite mixed graphs, which strengthens some results of Ries and de Werra (2008) [9].

Université de Fribourg

How do the global stock markets influence one another? Evidence from finance big data and Granger causality directed network

Tang, Yong ; Xiong, Jason Jie ; Luo, Yong ; Zhang, Yi-Cheng

In: International Journal of Electronic Commerce, 2019, vol. 23, no. 1, p. 85–109

The recent financial network analysis approach reveals that the topologies of financial markets have an important influence on market dynamics. However, the majority of existing Finance Big Data networks are built as undirected networks without information on the influence directions among prices. Rather than understanding the correlations, this research applies the Granger causality test to...

Université de Fribourg

Complexities in financial network topological dynamics: modeling of emerging and developed stock markets

Tang, Yong ; Xiong, Jason Jie ; Jia, Zi-Yang ; Zhang, Yi-Cheng

In: Complexity, 2018, p. -

Policy makings and regulations of financial markets rely on a good understanding of the complexity of financial markets. There have been recent advances in applying data-driven science and network theory into the studies of social and financial systems. Financial assets and institutions are strongly connected and influence each other. It is essential to study how the topological structures of...

Université de Fribourg

Competition may increase social utility in bipartite matching problem

Kong, Yi-Xiu ; Yuan, Guang-Hui ; Zhou, Lei ; Wu, Rui-Jie ; Shi, Gui-Yuan

In: Complexity, 2018, p. -

Bipartite matching problem is to study two disjoint groups of agents who need to be matched pairwise. It can be applied to many real-world scenarios and explain many social phenomena. In this article, we study the effect of competition on bipartite matching problem by introducing conformity into the preference structure. The results show that a certain amount of competition can improve the...

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

Influencers identification in complex networks through reaction-diffusion dynamics

Iannelli, Flavio ; Mariani, Manuel S. ; Sokolov, Igor M.

In: Physical Review E, 2018, vol. 98, no. 6, p. 062302

A pivotal idea in network science, marketing research, and innovation diffusion theories is that a small group of nodes—called influencers—have the largest impact on social contagion and epidemic processes in networks. Despite the long-standing interest in the influencers identification problem in socioeconomic and biological networks, there is not yet agreement on which is the best...