Recommender systems

Lü, Linyuan; Medo, Matúš; Yeung, Chi Ho; Zhang, Yicheng; Zhang, Zi-Ke; Zhou, Tao

doi:10.1016/j.physrep.2012.02.006

Cited by 901 publications

(521 citation statements)

References 256 publications

(351 reference statements)

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“…Bipartite networks representing online systems typically consist of user-and item-nodes with connections between them drawn when a user has collected, bought, rated, or otherwise interacted with an item. The item degree distribution is usually broad and often exhibits a power-law shape [30]. This is a direct consequence of the preferential attachment process [19], which occurs in many real networks, such as scientific collaboration networks [33], metabolic networks [34] and social networks [35].…”

Section: Resultsmentioning

confidence: 99%

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

Vidmer

Zeng

Medo

et al. 2015

Physica A: Statistical Mechanics and its Applications

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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 products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.

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Section: Resultsmentioning

confidence: 99%

“…If the two methods are coupled together, the resulting hybrid diffusion method is one of the best performing link prediction method in bipartite networks without explicit rating of items [30].…”

Section: Link Predictionmentioning

confidence: 99%

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

Vidmer

Zeng

Medo

et al. 2015

Physica A: Statistical Mechanics and its Applications

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“…recommendation" in which network structure information is used to generate recommendation list to online users [9][10][11]. This field aims at predicting the future links for each individual.…”

Section: E-mail Addressesmentioning

confidence: 99%

Identifying missing and spurious connections via the bi-directional diffusion on bipartite networks

2014

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Link prediction and spurious link detection in complex networks have attracted increasing attention from both physical and computer science communities, due to their wide applications in many real systems. Related previous works mainly focus on monopartite networks while these problems in bipartite networks are not yet systematically addressed. Containing two different kinds of nodes, bipartite networks are essentially different from monopartite networks, especially in node similarity calculation: the similarity between nodes of different kinds (called inter-similarity) is not well defined. In this letter, we employ the local diffusion processes to measure the inter-similarity in bipartite networks. We find that the inter-similarity is asymmetric if the diffusion is applied in different directions. Accordingly, we propose a bi-directional hybrid diffusion method which is shown to achieve higher accuracy than the existing diffusion methods in identifying missing and spurious links in bipartite networks.Link prediction and spurious link detection problems are longstanding challenges in network study [1]. They have applications in many fields such as chemistry, biology, sociology and computer science. In many biological networks, such as food webs, proteinprotein interaction networks and metabolic networks, whether a link between two nodes exists must be demonstrated by field or laboratorial experiments, which is usually very costly [2]. In addition, the data in constructing biological and social networks may contain inaccurate information, resulting in spurious links [3]. Correcting the network connections can be very expensive if it is done by laboratorial experiments. To solve this problem, many networkbased methods have been developed and they are shown to have high accuracy in identifying missing and spurious links [4].So far, most related works focus on monopartite networks in which only one type of nodes exists [5]. However, many systems coupled by two different building blocks should be modeled by bipartite networks. For example, the e-commercial systems consisting of online users and items [6], the scientific collaboration system consisting of authors and papers [7], family name inheritance system consisting of babies and names [8] are naturally described by such networks. The link prediction and spurious link detection problems are not yet well addressed in bipartite networks. One close problem is the so-called "network-based * Corresponding authors.

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“…The fundamental reason is that the network structure could well describe the interacting pattern of individual elements, which leads to many complex phenomena in biological, social, economic, communication, transportation and physical systems. Characterizing the structural features [3][4][5] is the foundation of the correct understanding about the dynamics of networks [6][7][8][9], the dynamics on networks (e.g., epidemic spreading [10,11], transportation [12,13], evolutionary game [14,15], synchronization [16], and other social and physical processes [17,18]), as well as the network-related applications [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Lower bound of assortativity coefficient in scale-free networks

Yang

Pan

Zhou

2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The degree-degree correlation is important in understanding the structural organization of a network and the dynamics upon a network. Such correlation is usually measured by the assortativity coefficient r, with natural bounds r ∈ [−1, 1]. For scale-free networks with power-law degree distribution p(k) ∼ k −γ , we analytically obtain the lower bound of assortativity coefficient in the limit of large network size, which is not -1 but dependent on the power-law exponent γ. This work challenges the validation of assortativity coefficient in heterogeneous networks, suggesting that one cannot judge whether a network is positively or negatively correlated just by looking at its assortativity coefficient.

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Recommender systems

Cited by 901 publications

References 256 publications

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

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

Identifying missing and spurious connections via the bi-directional diffusion on bipartite networks

Lower bound of assortativity coefficient in scale-free networks

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