Matching user accounts with spatio-temporal awareness across social networks

Li, Yongjun; Ji, Wei; Gao, Xing; Deng, Yao; Dong, Wei; Li, Dongxu

doi:10.1016/j.ins.2021.04.030

Cited by 14 publications

(5 citation statements)

References 23 publications

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“…User identification can be considered as a special case of user similarity measure. Li et al (2021) jointly considered temporal and spatial information in user activities and proposed a novel user identification method across social networks. The effect of user identification can be improved by fully extracting trajectory features and constructing a better similarity measure.…”

Section: Related Workmentioning

confidence: 99%

Section: Rel Ated Workmentioning

confidence: 99%

“…In real‐world applications, trajectory data are usually recorded by multiple devices jointly and the process of information collection is independent. Relevant research shows that the mobile trajectory, app usage, and other behavior data can reflect the uniqueness of users (Hao et al, 2016; Li et al, 2021; Zang & Bolot, 2011). The target of mobile user identification is to find different mobile devices of the same user.…”

Section: Introductionmentioning

confidence: 99%

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Mobile user identification based on road networks and multiple distance measures

Fan

Wang

et al. 2023

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Mobile user identification aims at matching different mobile devices of the same user using trajectory data, which has attracted extensive research in recent years. Most of the previous work extracted trajectory features based on regular grids, which will lead to incorrect feature representation due to lack of geographic information. Besides, most trajectory similarity models only considered one single distance measure to calculate the similarity between users, which ignore the connection between different distance measures and may lead to some false matches. In light of this, we present a novel user identification method based on road networks and multiple distance measures in this article. The proposed method segments a city map into several grids and road segments based on road networks. Then it extracts location and road information of trajectories to jointly construct user features. Multiple distance measures are fused by a discriminant model to improve the effect of user identification. Experiments on real GPS trajectory datasets show that our proposed method outperforms related similarity measure methods and is stable for mobile user identification. Meanwhile, our method can also achieve good identification results even on sparse trajectory datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Rel Ated Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mobile user identification based on road networks and multiple distance measures

Fan

Wang

et al. 2023

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“…Zhou et al [36] proposed capturing node distribution in Wasserstein space and reformulating the UA task as an optimal network transport problem in a fully unsupervised manner. Recently, Li et al [37] studied user's check-in records, and jointly considered user's spatial-temporal information (e.g., location and time) to link identical users without any annotations.…”

Section: Related Workmentioning

confidence: 99%

JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks

Yang

Chen²

2022

Applied Sciences

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User alignment (UA), a central issue for social network analysis, aims to recognize the same natural persons across different social networks. Existing studies mainly focus on the positive effects of incorporating user attributes and network structure on UA. However, there have been few in-depth studies into the existing challenges for the joint integration of different types of text attributes, the imbalance between user attributes and network structure, and the utilization of massive unidentified users. To this end, this paper presents a high-accuracy embedding model named Joint embedding of Attributes and Relations for User Alignment (JARUA), to tackle the UA problem. First, a mechanism that can automatically identify the granularity of user attributes is introduced for handling multi-type user attributes. Second, a graph attention network is employed to extract the structural features and is integrated with user attributes features. Finally, an iterative training algorithm with quality filters is introduced to bootstrap the model performances. We evaluate JARUA on two real-world data sets. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art approaches.

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“…Trajectory data have the characteristics of uneven granularity, wide range, and long duration, which has attracted a large number of researchers to analysis and study. The purpose of trajectory data mining is to extract and discover unknown and useful information from these massive data sources and provide auxiliary decision‐making functions for various practical applications (Gong et al., 2011), such as traffic analysis (Hefez et al., 2011; Krogh et al., 2013; Li et al., 2014), travel recommendation (Adomavicius & Tuzhilin, 2005; Bao et al., 2012; Dai et al., 2015; Lu et al., 2012; Zhang et al., 2014), and human mobility patterns recognition (Jiang et al., 2017; Li et al., 2021; Tkacik & Kordík, 2016; Xia et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

A novel trajectory similarity measurement method based on node‐sequence hierarchical digraph

Fan,

Wang,

Wang

et al. 2023

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Trajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node‐Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time‐Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T‐Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%.

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Matching user accounts with spatio-temporal awareness across social networks

Cited by 14 publications

References 23 publications

Mobile user identification based on road networks and multiple distance measures

Mobile user identification based on road networks and multiple distance measures

JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks

A novel trajectory similarity measurement method based on node‐sequence hierarchical digraph

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