Université de Fribourg

A fast algorithm for predicting links to nodes of interest

Chen, Bolun ; Chen, Ling ; Li, Bin

In: Information Sciences, 2016, vol. 329, p. 552–567

The problem of link prediction has recently attracted considerable attention in various domains, such as sociology, anthropology, information science, and computer science. In many real world applications, we must predict similarity scores only between pairs of vertices in which users are interested, rather than predicting the scores of all pairs of vertices in the network. In this paper, we...

Université de Fribourg

Identifying influential nodes in complex networks

Lü, Linyuan ; Shang, Ming-Sheng ; Zhang, Yi-Cheng ; Zhou, Tao

In: Physica A: Statistical Mechanics and its Applications, 2011, vol. 391, no. 4, p. 1777–1787

Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational...

Université de Fribourg

Detecting community structure in complex networks via node similarity

Pan, Ying ; Li, De-Hua ; Liu, Jian-Guo ; Liang, Jing-Zhang

In: Physica A: Statistical Mechanics and its Applications, 2010, vol. 389, no. 14, p. 2849-2857

The detection of the community structure in networks is beneficial to understand the network structure and to analyze the network properties. Based on node similarity, a fast and efficient method for detecting community structure is proposed, which discovers the community structure by iteratively incorporating the community containing a node with the communities that contain the nodes with...

Université de Fribourg

Collaborative filtering based on multi-channel diffusion

Shang, Ming-Sheng ; Jin, Ci-Hang ; Zhou, Tao ; Zhang, Yi-Cheng

In: Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, no. 23, p. 4867-4871

In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user–object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the number of channels being equal to the number of different ratings. Each channel represents a certain...

Université de Fribourg

Prediction in complex systems: the case of the international trade network

Vidmer, Alexandre ; Zeng, An ; Medo, Matúš ; Zhang, Yi-Cheng

In: Physica A: Statistical Mechanics and its Applications, 2015, vol. 436, p. 188–199

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...

Université de Fribourg

Ranking scientific publications: the effect of nonlinearity

Yao, Liyang ; Wei, Tian ; Zeng, An ; Fan, Ying ; Di, Zengru

In: Scientific Reports, 2014, vol. 4, p. -

Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to...

Université de Fribourg

Similarity-based classification in partially labeled networks

Zhang, Qian-Ming ; Shang, Ming-Sheng ; Lü, Linyuan

In: International Journal of Modern Physics C, 2010, vol. 21, no. 6, p. 813-824

Two main difficulties in the problem of classification in partially labeled networks are the sparsity of the known labeled nodes and inconsistency of label information. To address these two difficulties, we propose a similarity-based method, where the basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten...

Université de Fribourg

Uncovering missing links with cold ends

Zhu, Yu-Xiao ; Lü, Linyuan ; Zhang, Qian-Ming ; Zhou, Tao

In: Physica A: Statistical Mechanics and its Applications, 2012, vol. 139, no. 22, p. 5769–5778

To evaluate the performance of prediction of missing links, the known data are randomly divided into two parts, the training set and the probe set. We argue that this straightforward and standard method may lead to terrible bias, since in real biological and information networks, missing links are more likely to be links connecting low-degree nodes. We therefore study how to uncover missing links...