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Seed selection for information cascade in multilayer networks
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-3219-9598
Wrocƚaw University of Technology, POL.ORCID iD: 0000-0002-6474-0089
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-8929-7220
2017 (English)In: Complex Networks & Their Applications VI: Proceedings of the 6th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2017), Springer-Verlag New York, 2017Chapter in book (Refereed)
Abstract [en]

Information spreading is an interesting field in the domain of online social media. In this work, we are investigating how well different seed selection strategies affect the spreading processes simulated using independent cascade model on eighteen multilayer social networks. Fifteen networks are built based on the user interaction data extracted from Facebook public pages and tree of them are multilayer networks downloaded from public repository (two of them being Twitter networks). The results indicate that various state of the art seed selection strategies for single-layer networks like K-Shell or VoteRank do not perform so well on multilayer networks and are outperformed by Degree Centrality.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2017.
Series
Studies in Computational Intelligence, ISSN 1860-949X
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-15326OAI: oai:DiVA.org:bth-15326DiVA: diva2:1148734
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2017-11-15Bibliographically approved
In thesis
1. Human Interactions on Online Social Media: Collecting and Analyzing Social Interaction Networks
Open this publication in new window or tab >>Human Interactions on Online Social Media: Collecting and Analyzing Social Interaction Networks
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Online social media, such as Facebook, Twitter, and LinkedIn, provides users with services that enable them to interact both globally and instantly. The nature of social media interactions follows a constantly growing pattern that requires selection mechanisms to find and analyze interesting data. These interactions on social media can then be modeled into interaction networks, which enable network-based and graph-based methods to model and understand users’ behaviors on social media. These methods could also benefit the field of complex networks in terms of finding initial seeds in the information cascade model. This thesis aims to investigate how to efficiently collect user-generated content and interactions from online social media sites. A novel method for data collection that is using an exploratory research, which includes prototyping, is presented, as part of the research results in this thesis.

 

Analysis of social data requires data that covers all the interactions in a given domain, which has shown to be difficult to handle in previous work. An additional contribution from the research conducted is that a novel method of crawling that extracts all social interactions from Facebook is presented. Over the period of the last few years, we have collected 280 million posts from public pages on Facebook using this crawling method. The collected posts include 35 billion likes and 5 billion comments from 700 million users. The data collection is the largest research dataset of social interactions on Facebook, enabling further and more accurate research in the area of social network analysis.

 

With the extracted data, it is possible to illustrate interactions between different users that do not necessarily have to be connected. Methods using the same data to identify and cluster different opinions in online communities have also been developed and evaluated. Furthermore, a proposed method is used and validated for finding appropriate seeds for information cascade analyses, and identification of influential users. Based upon the conducted research, it appears that the data mining approach, association rule learning, can be used successfully in identifying influential users with high accuracy. In addition, the same method can also be used for identifying seeds in an information cascade setting, with no significant difference than other network-based methods. Finally, privacy-related consequences of posting online is an important area for users to consider. Therefore, mitigating privacy risks contributes to a secure environment and methods to protect user privacy are presented.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2018
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 1
Keyword
Social Media, Social Networks, Crawling, Complex Networks, Information Cascade, Seed Selection, Privacy
National Category
Computer Science
Identifiers
urn:nbn:se:bth-15503 (URN)978-91-7295-344-4 (ISBN)
Public defence
2017-01-15, J1650, Karlskrona, 13:00 (English)
Opponent
Supervisors
Available from: 2017-11-23 Created: 2017-11-15 Last updated: 2017-12-06Bibliographically approved

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Borg, Anton

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