Social Network Influence Ranking via Embedding Network Interactions for User Recommendation

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Abstract

Within social networks user influence may be modelled based on user interactions. Further, it is typical to recommend users to others. What is the role of user influence in user recommendation? In this paper, we first propose to use a node embedding approach to integrate many types of interaction into embedded spaces where we then define a novel closeness measure to quantify the closeness of users based on interactions. We then propose a new influence ranking algorithm based on PageRank by incorporating the closeness
measure into the ranking mechanism. We evaluate our algorithm, EIRank, using a dataset collected from Twitter. Our experimental results show that our algorithm measures user influence better by way of a user recommendation task, where our algorithm outperforms TwitterRank across a range of experimental network settings.
Original languageEnglish
Title of host publicationWWW '20
Subtitle of host publicationCompanion Proceedings of the Web Conference 2020
EditorsAmal El Fallah Seghrouchni, Gita Sukthankar, Tie-Yan Liu, Maarten van Steen
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages379-384
Number of pages6
ISBN (Print) 978-1-4503-7024-0
DOIs
Publication statusPublished - 1 Apr 2020
Event4th International Workshop on Mining Actionable Insights from Social Networks - Taipei, Taiwan
Duration: 20 Apr 202024 Apr 2020
Conference number: 4
https://www.maisonworkshop.org/

Workshop

Workshop4th International Workshop on Mining Actionable Insights from Social Networks
Abbreviated titleMAISoN 2020
CountryTaiwan
CityTaipei
Period20/04/2024/04/20
Internet address

Cite this

Bo, H., McConville, R., Hong, J., & Liu, W. (2020). Social Network Influence Ranking via Embedding Network Interactions for User Recommendation. In A. E. F. Seghrouchni, G. Sukthankar, T-Y. Liu, & M. van Steen (Eds.), WWW '20: Companion Proceedings of the Web Conference 2020 (pp. 379-384). Association for Computing Machinery (ACM). https://doi.org/10.1145/3366424.3383299