No exact match found for Zhang Qian-Ming, using Zhang Qian Ming instead...
Université de Fribourg

Extracting the Information Backbone in Online System

Zhang, Qian-Ming ; Zeng, An ; Shang, Ming-Sheng

In: PLoS ONE, 2013, vol. 8, no. 5, p. e62624

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite...

Université de Fribourg

Potential theory for directed networks

Zhang, Qian-Ming ; Lü, Linyuan ; Wang, Wen-Qiang ; Yu-Xiao ; Zhou, Tao

In: PLoS ONE, 2013, vol. 8, no. 2, p. e55437

Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. In particular, the microscopic organizing principles of directed networks are less understood than those of undirected networks. This article proposes a hypothesis named potential theory, which assumes that every directed link corresponds to a decrease of a unit potential and...

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...

Université de Fribourg

Link prediction in complex networks: a local naïve Bayes model

Liu, Zhen ; Zhang, Qian-Ming ; Lü, Linyuan ; Zhou, Tao

In: Europhysics Letters, 2011, vol. 96, no. 4, p. 48007

The common-neighbor–based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In the traditional method, each common neighbor of two nodes contributes equally to the connection likelihood. In this letter, we argue that different common neighbors may play different roles and thus contributes...

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

Empirical comparison of local structural similarity indices for collaborative-filtering-based recommender systems

Zhang, Qian-Ming ; Shang, Ming-Sheng ; Zeng, Wei ; Chen, Yong ; Lü, Linyuan

In: Physics Procedia, 2010, vol. 3, no. 5, p. 1887-1896

Collaborative filtering is one of the most successful recommendation techniques, which can effectively predict the possible future likes of users based on their past preferences. The key problem of this method is how to define the similarity between users. A standard approach is using the correlation between the ratings that two users give to a set of objects, such as Cosine index and Pearson...