Analysis of Proximity Informed User Behavior in a Global Online Social Network
Despite the earlier claim of "Death of Distance", recent studies revealed that geographical proximity still greatly influences link formation in online social networks. However, it is unclear how physical distances are intertwined with users' online behaviors in a virtual world. We study the role of spatial dependence on a global online social network with a dyadic Logit model. Results show country-specific patterns for distance effect on probabilities to build connections. Effects are stronger when the possibility for two people to meet in person exists. Relative to weak ties, dependence on proximity is looser for strong social ties.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Proximity-Informed Calibration for Deep Neural Networks
Confidence calibration is central to providing accurate and interpretable uncertainty estimates, especially under safety-critical scenarios. However, we find that existing calibration algorithms often overlook the issue …
Interest Networks (iNETs) for Cities: Cross-Platform Insights and Urban Behavior Explanations
Location-Based Social Networks (LBSNs) provide a rich foundation for modeling urban behavior through iNETs (Interest Networks), which capture how user interests are distributed throughout urban spaces. This study compare…
Explainable RecommendationRecommendation SystemsAutomated Lane Detection in Crowds using Proximity Graphs
Studying the behavior of crowds is vital for understanding and predicting human interactions in public areas. Research has shown that, under certain conditions, large groups of people can form collective behavior pattern…
Lane DetectionC-STEP: Continuous Space-Time Empowerment for Physics-informed Safe Reinforcement Learning of Mobile Agents
Safe navigation in complex environments remains a central challenge for reinforcement learning (RL) in robotics. This paper introduces Continuous Space-Time Empowerment for Physics-informed (C-STEP) safe RL, a novel meas…
Reinforcement LearningCollision AvoidanceAnalysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms
In this work we study the behavior of the forward-backward (FB) algorithm when the proximity operator is replaced by a sub-iterative procedure to approximate a Gaussian denoiser, in a Plug-and-Play (PnP) fashion. In part…
Compressive SensingDenoisingImage Restoration