Name
Abstract | ||
|---|---|---|
The tie strength is a network concept that has attracted arguably the most research attention as being an important ingredient for modeling the interaction of users in a network and understanding their behavior. With the emergence of online social networks like Facebook and Twitter, the social tie strength interpretation evolved to reflect the frequency of contact on computer-mediated communication networks. The rapid proliferation of Mobile Adhoc Networks and in particular vehicular networks creates ample opportunity for novel applications relying on the human mobility characteristics such as vehicles destination prediction and recommendation systems. Hence, arises the need for a novel definition of the social tie strength reflecting the meetings frequency of the network nodes. This paper sets the ground work for quantifying the social tie strength in vehicular social networks. It presents a new definition for the social tie strength and formalizes a semantic aware model namely the Social, Spatial and Context-based Encounter Frequency (SSCEF) to quantify the strength as per the suggested definition. The model is tested using a data-set collected at the city of Ulm, Germany for the purpose of this study. It comprises social network information and its associated one month mobility traces. The performance of the proposed model is further validated by feeding the inferred ties to a social-based vehicular destination predictor [4]. The SSCEF inferred ties achieves a prediction accuracy of 67% in comparison to 70% for the original traces-based calculated ties. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WD.2019.8734218 | 2019 Wireless Days (WD) |
Keywords | Field | DocType |
Predictive models,Frequency measurement,Pollution measurement,Semantics,Facebook,Context modeling | Recommender system,Social network,Telecommunications network,Computer science,Inference,Node (networking),Computer network,Context model,Vehicular ad hoc network,Semantics | Conference |
ISSN | ISBN | Citations |
2156-9711 | 978-1-7281-0117-0 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nardine Basta | 1 | 1 | 1.36 |
| Amal El-nahas | 2 | 41 | 6.47 |
| Hans Peter Großmann | 3 | 65 | 8.64 |
| Slim Abdennadher | 4 | 394 | 60.95 |