Pairwise Link Prediction Model for Out of Vocabulary Knowledge Base Entities

Accurate prediction of train delay recovery is critical for railway incident management and providing passengers with accurate journey time. In this paper, a two-stage prediction model is proposed to predict the recovery time of train primary-delay based on the real records from High-Speed Railway (HSR). In Stage 1, two models are built to study the influence of feature space and model framework on the prediction accuracy of buffer time in each section or station. It is found that explicitly inputting the attribute features of stations and sections to the model, instead of implicit simulation, will improve the prediction accuracy effectively. For validation purpose, the proposed model has been compared with several alternative models, namely, Logistic Regression (LR), Artificial Neutral Network (ANN), Support Vector Machine (SVM) and Gradient Boosting Tree (GBT). The results show that its remarkable performance is better than other schemes. Specifically, when the error is extended to 3[Formula: see text]min, the proposed model can achieve up to the accuracy of 94.63%. It proves that our method has high value in practical engineering application. Considering the delay propagation of trains is a complex process, our future study will focus on building delay propagation knowledge base and dispatcher experience knowledge base.

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Enhancing link prediction by exploring community membership of nodes

International Journal of Modern Physics B ◽

10.1142/s021797921950382x ◽

2019 ◽

Vol 33 (31) ◽

pp. 1950382

Author(s):

Shenshen Bai ◽

Shiyu Fang ◽

Longjie Li ◽

Rui Liu ◽

Xiaoyun Chen

Keyword(s):

Community Structure ◽

Prediction Model ◽

Link Prediction ◽

Prediction Accuracy ◽

Network Data ◽

Data Link ◽

Structure Information ◽

Community Membership

With the proliferation of available network data, link prediction has become increasingly important and captured growing attention from various disciplines. To enhance the prediction accuracy by making full use of community structure information, this paper proposes a new link prediction model, namely CMS, in which different community memberships of nodes are investigated. In the opinion of CMS, different memberships can have different influence to link’s formation. To estimate the connection likelihood between two nodes, the CMS model weights the contribution of each shared neighbor according to the corresponding community membership. Three CMS-based methods are derived by introducing three forms of contribution that neighbors make. Extensive experiments on 12 networks are conducted to evaluate the performance of CMS-based methods. The results manifest that CMS-based methods are more effective and robust than baselines.

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Hidden Link Prediction in Criminal Networks Using the Deep Reinforcement Learning Technique

Computers ◽

10.3390/computers8010008 ◽

2019 ◽

Vol 8 (1) ◽

pp. 8 ◽

Cited By ~ 7

Author(s):

Marcus Lim ◽

Azween Abdullah ◽

NZ Jhanjhi ◽

Mahadevan Supramaniam

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Network Analysis ◽

Prediction Model ◽

Supervised Learning ◽

Link Prediction ◽

Supervised Machine Learning ◽

Criminal Networks ◽

Criminal Network ◽

Learning Technique

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

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