Tata Institute of Fundamental Research

From Telecom to Social Networks: Popularity and Shaping Activity in Timeline Networks

STCS Seminar
Speaker: Alexandre Reiffers-Masson (Indian Institute of Science Robert Bosch Centre for Cyberphysical Systems Bengaluru, Karnataka 560012)
Organiser: Sandeep K Juneja
Date: Tuesday, 4 Sep 2018, 16:00 to 17:00
Venue: A-201 (STCS Seminar Room)

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Abstract:  In this presentation, we adapt mathematical tools and models from Telecommunication Networks to Online Social networks. We will focus our presentation on two main issues in OSNs, algorithms for shaping timelines and popularity maximization in OSNs. This presentation is based on two papers:
Timelines are publisher-driven caches: Analyzing and shaping timeline networks (NETECON 2017) Cache networks are one of the building blocks of information-centric networks (ICNs). Most of the recent work on cache networks has focused on networks of request-driven caches, which are populated based on users requests for content generated by publishers. However, user-generated content still poses the most pressing challenges. For such content timelines are the de facto sharing solution. In this paper, we establish a connection between timelines and publisher-driven caches. We propose simple models and metrics to analyze publisher-driven caches, allowing for variable-sized objects. Then, we design two efficient algorithms for timeline workload shaping, leveraging admission and price control in order, for instance, to aid service providers to attain prescribed service level agreements.
A Generalized Fractional Program for Maximizing Content Popularity in Online Social Networks (FAB/ASONAM 2018) In this paper, we consider a "generalized" fractional program in order to solve a popularity optimization problem in which a source of contents controls the topics of her contents and the rate with which posts are sent to a timeline. The objective of the source is to maximize its overall popularity in an Online Social Network (OSN). We propose an efficient algorithm that converges to the optimal solution of the Popularity maximization problem.