Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management 2020
DOI: 10.1145/3340531.3411867
|View full text |Cite
|
Sign up to set email alerts
|

WMEgo: Willingness Maximization for Ego Network Data Extraction in Online Social Networks

Abstract: The data of egocentric networks (ego networks) are very important for evaluating and validating the algorithms and machine learning approaches in Online Social Networks (OSNs). Nevertheless, obtaining the ego network data from OSNs is not a trivial task. Conventional manual approaches are time-consuming, and only a small number of users would agree to contribute their data. This is because there are two important factors that should be considered simultaneously for this data acquisition task: i) users' willing… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 17 publications
(2 citation statements)
references
References 33 publications
0
2
0
Order By: Relevance
“…Here, SVGD includes two additional constraints: i) Personal preference constraint θ, which requires that for a user u, any user v rendered in u's VR display must have a personal preference at least θ, i.e., p(u, v) ≥ θ, ∀u ∈ V, v ∈ A u . Following (Hsu, Shen, and Chang 2020;Hsu, Lan, and Shen 2018), the above constraint aims to meet the minimum required personal preferences to prevent users from becoming extremely dissatisfied. In SVGD, this personal preference constraint (referred to as preference constraint hereafter) avoids rendering users that receive a low personal preference; ii) Display slot k u for all u ∈ V , which specifies the maximum number of other users that can be rendered on user u's VR display, to prevent too many people being rendered on users' VR displays, making the displays too crowded 1 .…”
Section: Problem Formulation and Hardness Resultsmentioning
confidence: 99%
“…Here, SVGD includes two additional constraints: i) Personal preference constraint θ, which requires that for a user u, any user v rendered in u's VR display must have a personal preference at least θ, i.e., p(u, v) ≥ θ, ∀u ∈ V, v ∈ A u . Following (Hsu, Shen, and Chang 2020;Hsu, Lan, and Shen 2018), the above constraint aims to meet the minimum required personal preferences to prevent users from becoming extremely dissatisfied. In SVGD, this personal preference constraint (referred to as preference constraint hereafter) avoids rendering users that receive a low personal preference; ii) Display slot k u for all u ∈ V , which specifies the maximum number of other users that can be rendered on user u's VR display, to prevent too many people being rendered on users' VR displays, making the displays too crowded 1 .…”
Section: Problem Formulation and Hardness Resultsmentioning
confidence: 99%
“…Among the wide application spectrum of GNNs, link prediction is one of the most important tasks and has been receiving significant research attention as the connectivity of entities is the most essential component in a network. Application scenarios of link prediction include recommendation, e-commerce, friend recommendation in social networks, and much more [32,29,31,18,12,30].…”
Section: Introductionmentioning
confidence: 99%