How Often Should I Access My Online Social Networks?

Abstract

Users of online social networks are faced with a conundrum of trying to be always informed without having enough time or attention budget to do so. The retention of users on online social networks has important implications, encompassing economic, psychological and infrastructure aspects. In this paper, we pose the following question: what is the optimal rate at which users should access a social network? To answer this question, we propose an analytical model to determine the value of an access (VoA) to the social network. In the simple setting considered in this paper, VoA is defined as the chance of a user accessing the network and obtaining new content. Clearly, VoA depends on the rate at which sources generate content and on the filtering imposed by the social network. Then, we pose an optimization problem wherein the utility of users grows with respect to VoA but is penalized by costs incurred to access the network. Using the proposed framework, we provide insights on the optimal access rate. Our results are parameterized using Facebook data, indicating the predictive power of the approach.

Spatial Distribution of the Mean Peak Age of Information in Wireless Networks

“…The above works provide foundational understanding of temporal variations of AoI from the perspective of queuing theory for a point-to-point communication system. Inspired by these works, the AoI or similar age-related metrics have been used to characterize the performance of real-time monitoring services in a variety of communication systems, including broadcast networks [17]- [19], multicast networks [20], [21], multi-hop networks [22], [23], multi-server information-update systems [24], IoT networks [25]- [30], cooperative device-to-device (D2D) communication networks [31], [32], unmanned aerial vehicle (UAV)-assisted networks [33], [34], ultra-reliable low-latency vehicular networks [35], and social networks [36]- [38]. All these studies mostly focus on minimizing the AoI with the following design objectives: 1) design of scheduling policies [17]- [19], [22], [30], 2) design of cooperative transmission policies [20]- [23], [31], [32], 3) design of the status update sampling policies [27]- [29], [36], and 4) trade-off with other performances metrics in heterogeneous traffic/networks scenarios [23]- [25], [29], [37].…”