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Social Network Influence Ranking via Embedding Network Interactions for User Recommendation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationWWW ’20 Companion
Publisher or commissioning bodyAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Print) 978-1-4503-7024-0/20/04
DOIs
DateAccepted/In press - 12 Feb 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

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.

Event

4th International Workshop on Mining Actionable Insights from Social Networks

Abbreviated titleMAISoN 2020
Conference number4
Duration20 Apr 202024 Apr 2020
CityTaipei
CountryTaiwan
Web address (URL)
Degree of recognitionInternational event

Event: Workshop

Documents

Documents

  • Full-text PDF (accepted author manuscript)

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DOI

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