Abstract
Now, it is popular for people to share their feelings, activities tagged with geography and temporal information in Online Social Networks (OSNs). The spatial and temporal interactions occurred in OSNs contain a wealth of information to indicate friendship between persons. Existing researches generally focused on single dimension: spatial or temporal dimension. The simplified model only works in limited scenarios. Here, we aim to understand the probability of friendship and the place and time of interactions. First, spatial similarity of interactions is defined as a vector of places where persons checked in. Second, we employ exponential functions to characterize the change of strength of interactions as time goes on. Finally, a unified probability model to predict friendship between two persons is given. The model contains two sub-models based on spatial similarity and temporal similarity respectively. The experimental results on four data sets including spatial data sets (Gowalla and Weeplaces) and temporal data sets (Higgs Twitter Data set, High school Call Data set) show that our model works as expected.
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Acknowledgment
The research is supported by The National Key Research & Development Program of China (No. 2018YFB1004002), NSFC (No. 61672255), Science and Technology Planning Project of Guangdong Province, China (No. 2016B030306003 and 2016B030305002), and the Fundamental Research Funds for the Central Universities, HUST.
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Kou, Z., Wang, H., Yuan, P., Jin, H., Xie, X. (2019). Predicting Friendship Using a Unified Probability Model. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_5
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DOI: https://doi.org/10.1007/978-981-15-1899-7_5
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