Willingness Maximization for Ego Network Data Extraction in Multiple Online Social Networks

Egocentric network (ego network) data are very important for evaluating 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 sometimes the ego network data are quite incomplete because only a small number of users would agree to provide their data. This is because there are two important factors that should be considered simultaneously for this data acquisition task: i) users’ willingness to provide their data, and ii) the structure of the ego network. However, addressing the above two factors to obtain the more complete ego network data has not received much research attention. Therefore, in this paper, we address this issue by proposing a family of new research problems. The first proposed problem, named
Willingness Maximization for Ego Network Extraction in Online Social Networks (WMEgo)
, identifies a set of ego networks from a single OSN, such that the willingness of the users to provide their data is maximized. We prove that WMEgo is NP-hard and propose a
$\frac{1}{2}(1-\frac{1}{e})$
-approximation algorithm, named
Ego Network Identification with Maximum Willingness (EIMW)
. Furthermore, we extend the idea of WMEgo to multiple social networks and formulate a new research problem, named
Willingness Maximization on Multiple Social Networks for Ego Network Extraction (WM$^{2}$2Ego)
, which is able to effectively obtain ego network data from multiple social networks
simultaneously
. We propose a
$\frac{1}{2}$
-approximation algorithm, named
Maximum Expansion for UNified EXpenses (MUNEX)
for a special case of WM
$^{2}$
Ego and then design a constant-ratio approximation algorithm to the general WM
$^{2}$
Ego problem, named
Maximum Expansion with Expense Examination (M3E)
. We conduct two evaluation studies with 672 and 1,052 volunteers to validate the proposed WMEgo and WM
$^{2}$
Ego problems, respectively, and show that they are able to obtain much more complete ego network data compared to other baselines. We also perform extensive experiments on multiple real datasets to demonstrate that the proposed approaches significantly outperform the other baselines.