Power-law strength-degree correlation from resource-allocation dynamics on weighted networks

Ou, Qing; Jin, Y.; Zhou, Tao; Wang, Bing–Hong; Yin, Baocai

doi:10.1103/physreve.75.021102

Cited by 185 publications

(95 citation statements)

References 29 publications

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“…A measure that takes into account the structural connectivity of a node by the number of edges incident to that node as well as the weights of those edges representing their functional role is the strength. A node's strength, s i , is the sum of the weights of edges incident to node i [10]. Node strength strongly correlates with energy dissipation and captures the strain localisation, as shown in Figure 1(right).…”

Section: Dem Datamentioning

confidence: 99%

Minimum cut and shear bands

Tordesillas

Cramer

Walker

2013

AIP Conference Proceedings

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Section: Dem Datamentioning

confidence: 99%

Minimum cut and shear bands

Tordesillas

Cramer

Walker

2013

AIP Conference Proceedings

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“…This index is motivated by the resource allocation dynamics on complex networks [48]. Consider a pair of nodes, x and y, which are not directly connected.…”

Section: Local Similarity Indicesmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

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2,384

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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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“…We use a diffusion process to obtain similarities between users [20,21]. The basic idea is shown in Fig.…”

Section: Diffusion-based Similaritymentioning

confidence: 99%

Collaborative filtering with diffusion-based similarity on tripartite graphs

Shang

Zhang

Zhou

et al. 2010

Physica A: Statistical Mechanics and its Applications

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Collaborative tags are playing more and more important role for the organization of information systems. In this paper, we study a personalized recommendation model making use of the ternary relations among users, objects and tags. We propose a measure of user similarity based on his preference and tagging information. Two kinds of similarities between users are calculated by using a diffusion-based process, which are then integrated for recommendation. We test the proposed method in a standard collaborative filtering framework with three metrics: ranking score, Recall and Precision, and demonstrate that it performs better than the commonly used cosine similarity.

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Power-law strength-degree correlation from resource-allocation dynamics on weighted networks

Cited by 185 publications

References 29 publications

Minimum cut and shear bands

Minimum cut and shear bands

Link prediction in complex networks: A survey

Collaborative filtering with diffusion-based similarity on tripartite graphs

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