| Title | Social Visibility Optimization in OSNs with Anonymity Guarantees: Modeling, Algorithms and Applications |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zheng, Shiyuan, Xie, Hong, Lui, John C.S. |
| Conference Name | 2021 IEEE 37th International Conference on Data Engineering (ICDE) |
| Keywords | anonymity, Approximation algorithms, composability, Computational modeling, Conferences, Data engineering, data structures, Human Behavior, KMV sketch, Metrics, Probabilistic logic, pubcrawl, resilience, Resiliency, social networking (online), social networks analysis, Social visibility |
| Abstract | Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of available users and invites them as new friends/followers so as to maximize his "social visibility". More importantly, the requester has to do this under the anonymity setting, which means he is not allowed to know the neighborhood information of these available users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as "VisMAX-A". Then we design an algorithmic framework named as "AdaExp", which adaptively expands the requester's visibility in multiple rounds. In each round of the expansion, AdaExp uses a query oracle with anonymity guarantee to select only one available user. By using probabilistic data structures like the k-minimum values (KMV) sketch, we design an efficient query oracle with anonymity guarantees. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms. |
| DOI | 10.1109/ICDE51399.2021.00201 |
| Citation Key | zheng_social_2021 |
Social Visibility Optimization in OSNs with Anonymity Guarantees: Modeling, Algorithms and Applications