Inferring Multilayer Spreading Networks in Online Social Media

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2021-08-23
Department
Major/Subject
Machine Learning, Data Science and Artificial Intelligence
Mcode
SCI3044
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
54 + 13
Series
Abstract
Beneath the item sharing activities in online social media underlies a spreading network that connects people of common interests and opinions, where items spread through its edges from one person to another. Often, this spreading network can be decomposed into multiple layers, each representing the spreading dynamics of a certain type of items, as classified by topic, attitude, language, etc. Such a multilayer spreading network is extremely valuable for understanding the system of item spreading, for it not only provides a more fine-grained and more correct model of people's item sharing behavior under different circumstances, but also reveals a division of items by their spreading dynamics. The problem is that even single layer spreading networks are usually not directly observable in online social media, let alone their multilayer decompositions. In this work, therefore, we focus on the task of automatically inferring a multilayer spreading network from the observable spreading logs of items. Building upon the theoretical model of a previous study, we propose a novel computational framework for conducting the inference, which we show to be both effective and efficient. In addition, we systematically analyze the performance of the inference framework under different network and cascade settings, thus providing a comprehensive reference for evaluating the applicability of the method to real-world datasets. We also briefly discuss how our results inspire further improvements of the framework.
Description
Supervisor
Kivelä, Mikko
Thesis advisor
Chen, Ted
Keywords
spreading, multilayer, network, inference
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Citation