Social account linking via weighted bipartite graph matching

Abstract

Along with the increasing popularity of online social network (OSN), it is common that the same user holds many accounts among different OSNs (eg, Facebook, Twitter, WeChat, QQ). In this scenario, an interesting and challenging problem arises: how to link accounts among OSNs belonged to a natural person, which is also known as a graph matching problem. The solution helps understand user behaviors and offer better services. To solve the account linking problem, various techniques for OSNs have been proposed. However, most existing methods assume specific OSN features impractical in general OSNs and unscalable to large-scale OSNs. To address these shortcomings, in this paper, we remodel the account linking problem into maximum matching on weighted bipartite graphs and utilize the Kuhn-Munkres algorithm to solve it. In our solution, we capture user profile, user online time distribution, and user interest as features to describe user accounts and measure account similarity, which is used as weight of edge in bipartite graphs. Then, the maximum matching on weighted bipartite graphs is solved with the Kuhn-Munkres algorithm. The experiments conducted on the real datasets show that our solution outperforms the baseline methods with 11%, 17%, and 29% on average in precision, recall, and F1 score, respectively.

A Survey of Across Social Networks User Identification

“…The matching between the accounts can then be completed by using the relevant matching algorithm. When matching the accounts on the two SNs, a classic matching algorithm can be employed, such as a weighted bipartite graph matching algorithm [32], stable marriage matching [33], etc. Let n be the number of accounts to be matched, the time complexity of the bipartite graph maximum weight matching algorithm is O(n 3 ); By contrast, the time complexity of the stable marriage matching algorithm is O(n 2 ), which is relatively low.…”

“…However, this needs to calculate a weight allocation scheme for each source account in the SN, and thus its computational complexity increases drastically when there are more users in the SNs. Ma et al [32] proposed a joint learning model that combines user profile information, online time distribution and interest to analyze the similarities between user accounts and adjust the weight of the information used by balancing factors. In addition, they also designed a KM algorithm-based user identification method and weighted bipartite graph maximum matching, and extended the application of KM algorithm.…”

Deep Learning-Based Scheduling Scheme for IEEE 802.15.4e TSCH Network

“…The vertexes of the upper side are considered subgroups of noninterfering links, and the lower vertexes are considered as the cells of slot-frame matrix. The edge weight is computed by summating normalized throughput and normalized delay to ensure fairness (details will be discussed in Section 3) [19]. The throughput provides the maximum data transfer, and delay is considered to ensure the reliability.…”