Abstract
Stochastic Internet of Things (IoT)-based communication behavior of the progressing world is tremendously impacting social networks. The growth of social networks helps to quantify the effect on the Social Internet of Things (SIoT). Multiple existences of two persons at several geographical locations in different time frames hint to predict the social connection. We investigate the extent to which social ties between people can be inferred by critically reviewing the social networks. Our study used Chinese telecommunication-based anonymized caller data records (CDRs) and two openly available location-based social network data sets, Brightkite and Gowalla. Our research identified social ties based on mobile communication data and further exploits communication reasons based on geographical location. This paper presents an inference framework that predicts the missing ties as suspicious social connections using pipe and filter architecture-based inference framework. It highlights the secret relationship of users, which does not exist in real data. The proposed framework consists of two major parts. Firstly, users’ cooccurrence based on the mutual location in a specific time frame is computed and inferred as social ties. Results are investigated based upon the cooccurrence count, the gap time threshold values, and mutual friend count values. Secondly, the detail about direct connections is collected and cross-related to the inferred results using Precision and Recall evaluation measures. In the later part of the research, we examine the false-positive results methodically by studying the human cooccurrence patterns to identify hidden relationships using a social activity. The outcomes indicate that the proposed approach achieves comprehensive results that further support the theory of suspicious ties.
Highlights
A social network is a web of social ties among individuals
In the second phase of research, we explore the falsepositive results formed by the caller data records (CDRs)-based social tie inference model
We initially investigated direct social ties formed by CDR data sets and compared them to the indirect social ties formed based on common location using Algorithm 1
Summary
A social network is a web of social ties among individuals. Social ties are the kind of one-to-one communication links among humans or Social Internet of ings [1, 2]. We develop a framework that infers existing social ties and the hidden relationships in a social network. We correlate results with the direct calls based on social connection using Precision and Recall evaluation measures. In the second phase of research, we explore the falsepositive results formed by the CDR-based social tie inference model. We state a missing tie as a suspicious tie between two people if they do not have any direct calls but are found together numerous times They have a certain number of mutual friends. We correlate the CDR-based social tie inference model’s false-positive results with the activity and simulation results. E inference model is tested on the CDR-based social network, Brightkite, and Gowalla, using Precision and Recall measures. Social ties coupling and predicting the mobility of users were researched by
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
