A Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network

Liao, Ziliang; Liu, Linlan; Chen, Yubin

doi:10.1109/access.2020.2967407

Cited by 12 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Like Fresh Protocol, ML-Fresh Protocol works on Euclidean space. The Formula for calculating Euclidean Distance (ED) [39] between each pair of nodes (a, b) in Set N is…”

Section: A) Warm-up Phase B) Preparation Phase C) Prediction Phase D)...mentioning

confidence: 99%

ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

Garg¹,

Dixit²,

Sethi³

2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Opportunistic Networks (OppNets) is gaining popularity day-by-day due to their various applications in the real-life world. The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication. Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks (Manets) and Wireless Sensor Networks (WSNs), therefore, it inherits most of the traits from both mentioned networking techniques. Apart from its popularity, Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network. These issues lower the overall performance of the network. Keeping this thing in mind, ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz. pattern prediction, decision tree prediction, adamic-adar method for complex networks. Apart from this, ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background. Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service (QoS) parameters.

show abstract

“…Like Fresh Protocol, ML-Fresh Protocol works on Euclidean space. The Formula for calculating Euclidean Distance (ED) [39] between each pair of nodes (a, b) in Set N is…”

Section: A) Warm-up Phase B) Preparation Phase C) Prediction Phase D)...mentioning

confidence: 99%

ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

Garg¹,

Dixit²,

Sethi³

2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Community detection and link prediction are two different directions of social network analysis [31], while the former is used to mine network topology, the latter works based on the network structure to predict the future evolution trend in the social network. At present, a few researchers have tried to use community detection to improve the accuracy of link prediction.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

Shi

Liu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.

show abstract

“…Therefore, they are only suitable for the scenarios for which they were designed. In a recent publication [27], the authors proposed a link prediction approach for opportunistic networks based on random walk and a deep belief network. A predictive model is established based on a deep belief network which draw out the time-domain characteristics in the process of dynamic evolution of the opportunistic network.…”

Section: Related Workmentioning

confidence: 99%

Innovative mutual information‐based weighting scheme in stateless opportunistic networks

et al. 2021

View full text Add to dashboard Cite

Recently, opportunistic networks (OppNets) are considered as one of the most attractive developments of mobile ad hoc networks that have emerged, thanks to the development of intelligent devices. Owing to the harsh and dynamic topology of these networks, attaining high delivery ratio is a challenging issue. Hence, it is imperative to select which node's attribute must be adjusted to achieve a higher performance in such unpredictable networks. A mutual information-based weighting scheme (MIWS) that exploits the entropy concept to assess the impact of the nodes' attributes on the network performance was proposed. The weighting procedure aims to figure out the correlative relations between different attributes and delivery ratio performance of the network. The high weight of certain attributes implies a correspondingly high impact in achieving efficient data forwarding. The proposed scheme is proofed conceptually and simulated using the Opportunistic Network Environment simulator. In contrast to previous studies conducted in the context of weight resolution, the proposed approach allows us to address this issue in real-time stateless non-social OppNets. Regardless of the deployed routing protocol, experiments show that adjusting nodes' attributes based on the proposed MIWS can improve the performance up to encouraging delivery ratios. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

A Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network

Cited by 12 publications

References 25 publications

ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

Innovative mutual information‐based weighting scheme in stateless opportunistic networks

Contact Info

Product

Resources

About