Link prediction in social networks using Bayesian networks

Shalforoushan, Seyedeh Hamideh; Jalali, Mehrdad

doi:10.1109/aisp.2015.7123483

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Das and Das [24] considered the impacts of finegrained and coarse-grained time on links, and established a Markov prediction model based on time-varying graphs of social networks, which can better predict the connection of the node pairs in a dynamic network. Shalforoushan and Jalali [25] use a bayesian network to learn the relationships between variables, and propose friend recommendation method based on Bayesian network for a social network.…”

Section: B Probabilistic Model-based Prediction Methodsmentioning

confidence: 99%

Opportunistic Networks Link Prediction Method Based on Bayesian Recurrent Neural Network

Shu

2019

IEEE Access

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The goal of link prediction is to estimate the possibility of future links among nodes using known network information and the attributes of the nodes. According to the time-varying characteristics and the node's mobility of opportunistic networks, this paper proposes a novel link prediction method based on the Bayesian recurrent neural network (BRNN-LP) framework. The time series data of a dynamic opportunistic networks is sliced into snapshots in which there exist the correlation information and spatial location information. A vector of a snapshot is constructed based on such information, which represents the link information. Then, the vectors of multiple network snapshots constitute a spatiotemporal vector sequence. Benefiting from the BRNN's ability of extracting the features of time series data, the correlation between spatiotemporal vector sequence and node connection states is learned, and the law of the link evolution is captured to predict future links. The results on the MIT Reality dataset show that compared with methods such as the similarity-based indices, the support vector classifier, linear discriminant analysis and recurrent neural network, the proposed prediction method is more accurate and stable.INDEX TERMS Link prediction, opportunistic networks, Bayesian recurrent neural network.

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Section: B Probabilistic Model-based Prediction Methodsmentioning

confidence: 99%

Opportunistic Networks Link Prediction Method Based on Bayesian Recurrent Neural Network

Shu

2019

IEEE Access

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“…In [7], Papadimi-triou et al found the FriendLink Algorithm for fast and accurate link prediction in OSNs which outperforms many other related algorithms in terms of accuracy and time-complexity. Bayesian network has been also considered as a consistent model to understand the relations between future links to be predicted in networks [19], [20]. Recently, negative link prediction in social networks has attracted the attention of many researchers and considerable research work is being carried out to find efficient techniques for the same [15], [16], [18].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Link Prediction Technique in Social Networks based on Node Neighborhoods

Nandi¹,

Das²

2018

ijacsa

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The unparalleled accomplishment of social networking sites, such as Facebook, LinkedIn and Twitter has modernized and transformed the way people communicate to each other. Nowadays, a huge amount of information is being shared by online users through these social networking sites. Various online friendship sites such as Facebook and Orkut, allow online friends to share their thoughts or opinions, comment on others' timeline or photos, and most importantly, meet new online friends who were known to them before. However, the question remains as to how to quickly propagate one's online network by including more and more new friends. For this, one of the easy methods used is list of 'Suggested Friends' provided by these online social networking sites. For suggestion of friends, prediction of links for each online user is needed to be made based on studying the structural properties of the network. Link prediction is one of the key research directions in social network analysis which has attracted much attention in recent years. This paper discusses about a novel efficient link prediction technique LinkGyp and many other commonly used existing prediction techniques for suggestion of friends to online users of a social network and also carries out experimental evaluations to make a comparative analysis among each technique. Our results on three real social network datasets show that the novel LinkGyp link prediction technique yields more accurate results than several existing link prediction techniques.

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Link Prediction in Social Networks: An Edge Creation History-Retrieval Based Method that Combines Topological and Contextual Data

Cavalcante

Justel

Goldschmidt

2020

Intelligent Systems

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Link prediction in social networks using Bayesian networks

Cited by 4 publications

References 7 publications

Opportunistic Networks Link Prediction Method Based on Bayesian Recurrent Neural Network

Opportunistic Networks Link Prediction Method Based on Bayesian Recurrent Neural Network

An Efficient Link Prediction Technique in Social Networks based on Node Neighborhoods

Link Prediction in Social Networks: An Edge Creation History-Retrieval Based Method that Combines Topological and Contextual Data

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