Link Prediction of Social Networks Based on Weighted Proximity Measures

Xin, Xin; Li, Juanzi; Tang, Jie

doi:10.1109/wi.2007.52

Cited by 174 publications

(114 citation statements)

References 5 publications

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“…In addition, the path-dependent similarity indices should also be extended to take into account the link direction [149]. The fundamental task of link prediction in weighted networks, namely to predict the existence of links with the help of not only the observed links but also their weights, has already been considered by Murata et al [150] and Lü et al [151]. The former [150] suggested that the links with higher weights are more important in predicting missing links, while the latter [151] indicated a completely opposite conclusion: the weak links play a more significant role.…”

Section: Discussionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

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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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Section: Discussionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

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“…In their work, they extend eight benchmark unsupervised metrics for weighted networks, and adopt prediction scores as node pairs' attributes for a supervised classification model. Murata et al proposed a similar unsupervised metric that makes use of the weights of the existing links [23]; this outperforms traditional unsupervised methods especially when the target social networks are sufficiently dense. Experiments conducted on two real-world datasets (Yahoo!…”

Section: Related Workmentioning

confidence: 99%

“…However, proximities between nodes can be estimated better by using both graph proximity measures and the weights of existing links [22,23]. Much of this prior work uses the number of encounters between users as the link weights.…”

Section: Proposed Lpsf Framework: Reweighting the Network + Supervisementioning

confidence: 99%

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Link Prediction in Heterogeneous Collaboration Networks

Wang

Sukthankar

2014

Lecture Notes in Social Networks

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Abstract. Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this article, we study both supervised and unsupervised link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new supervised link prediction framework, Link Prediction using Social Features (LPSF ), which incorporates a reweighting scheme for the network based on nodes' features extracted from patterns of prominent interactions across the network.Experiments on coauthorship networks demonstrate that the choice for measuring link weights can be critical for the link prediction task. Our proposed reweighting method in LPSF better expresses the intrinsic relationship between nodes and improves prediction accuracy for supervised link prediction techniques. We also compare the unsupervised performance of the individual features used within LPSF with two new diffusion-based methods: LPDP (Link Prediction using Diffusion Process) and LPDM (Link Prediction using Diffusion Maps). Experiments demonstrate that LPDP is able to identify similar node pairs, even far away ones, that are connected by weak ties in the coauthorship network using the diffusion process; however, reweighting the network has little impact on prediction performance.

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“…In [15], Zhou and Scholkopf approached three related graph problems (classification, ranking and link prediction) in a new way. In [11], Murata and Moriyasu proposed three weighted similarity indices, as variants of the Common Neighbors, Adamic-Adar and Preferential Attachment indices, respectively. Some authors in [8] measured the performances of weighted and unweighted versions of Common neighbor, Adamic-Adar and Resource Allocation on real social, technological and biological networks, and found that sometimes the weighted indices perform even worse than unweighted indices.…”

Section: Literature Surveymentioning

confidence: 99%