Amin Mahmoudi
Abstract
The distance between users has an effect on the formation of social network ties, but it is not the only or even the main factor. Knowing all the features that influence such ties is very important for many related domains such as location-based recommender systems and community and event detection systems for online social networks (OSNs). In recent years, researchers have analyzed the role of user geo-location in OSNs. Researchers have also attempted to determine the probability of friendships being established based on distance, where friendship is not only a function of distance. However, some important features of OSNs remain unknown. In order to comprehensively understand the OSN phenomenon, we also need to analyze users’ attributes. Basically, an OSN functions according to four main user properties: user geo-location, user weight, number of user interactions, and user lifespan. The research presented here sought to determine whether the user mobility pattern can be used to predict users’ interaction behavior. It also investigated whether, in addition to distance, the number of friends (known as user weight) interferes in social network tie formation. To this end, we analyzed the above-stated features in three large-scale OSNs. We found that regardless of a high degree freedom in user mobility, the fraction of the number of outside activities over the inside activity is a significant fraction that helps us to address the user interaction behavior. To the best of our knowledge, research has not been conducted elsewhere on this issue. We also present a high-resolution formula in order to improve the friendship probability function.
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Index Terms
- The Relationship between Online Social Network Ties and User Attributes
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Reviews
Amos O Olagunju
Effective social promotion networks require reliable algorithms for discovering events and targeting their locations to online users. But what are the underlying factors that affect ties among online social network (OSN) users The authors examine the relationship between user characteristics and their OSN connections. The user traits investigated in the research include user weight (number of friends), number of check-ins, lifespan, "the maximum amount of movements made by each user," and density (number of check-ins per day). The inside and outside fraction (IO-fraction)-"density outside the home is divided by density inside the home"-is used to gauge the association between distance, user activities, and user attributes. Experiments performed on data from three large-scale, location-based social networks examine the effects of user characteristics on OSN connections; the results are used to develop an algorithm for computing the likelihood of friendship materialization in OSNs. The IOF is used to exhibit the associations between mobility and user characteristics. Data analysis results reveal that the movement of users is relevant to their OSN activities; the probability of a pair of users forming a friendship within a distance can be rationally computed; and friendship probability and user weight, activity, lifespan, and movement can be estimated. The authors clearly recognize that the datasets used for the various research investigations do not contain "a time labeled friendship graph and location information in each time for every user." Consequently, the study fails to show the establishment of social connections. In the face of this limitation, I call on computational statisticians and online marketing research analysts to read this interesting paper and to help identify more reliable datasets and accurate models for advertising specific events to customers based on individual behavior.
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