Social network influence ranking via embedding network interactions for user recommendation

Bo, Hongbo; McConville, Ryan; Hong, Jun; Liu, Weiru

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.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: WWW '20: The Web Conference 2020
• Start Date: Apr 20, 2020
• End Date: Apr 24, 2020
• Acceptance Date: Feb 12, 2020
• Online Publication Date: Apr 19, 2020
• Publication Date: Apr 20, 2020
• Deposit Date: Jun 7, 2021
• Pages: 379-384
• Book Title: WWW '20: Companion Proceedings of the Web Conference 2020
• ISBN: 9781450370240
• DOI: https://doi.org/10.1145/3366424.3383299
• Public URL: https://uwe-repository.worktribe.com/output/7450326