Users tend to own multiple accounts on different location-based social network (LBSN) platforms, and they typically engage with diverse social circles on each platform within the same locations. Consequently, linking these accounts across separate networks becomes essential, playing a critical role in information fusion. Previous works accomplishing user identity linkage (UIL) utilize individual mobility records, which are significantly affected by the issue of data scarcity. In this paper, we propose EgoMUIL, a heterogeneous graph embedding approach specifically devised for information propagation, aiming to alleviate the scarcity problem to some extent. Considering that follow relations of respective networks also hold great significance for the UIL task, we are inspired to enrich individual limited mobility records through follow relations. Our preliminary research reveals that direct common follow relations are quite insufficient. Since the followers with the same spatio-temporal mode tend to have social connections, we first mine closely-related users for each user through topology and locality similarity, generating respective cross-domain ego-networks. Subsequently, we construct a heterogeneous ego-mo hypergraph consisting of mobility and ego-networks. We propose a novel graph convolutional network (GCN)-based approach to learn user representations, which enables the aggregation of information from surrounding nodes, incorporating topological similarities, stay locality similarities, and co-occurrence frequencies. The resulting embeddings provide comprehensive representations of users and locations, capturing their characteristics and relationships across platforms, which further facilitates the UIL task. Our experimental results on real-world check-in datasets from Foursquare and Twitter demonstrate that EgoMUIL outperforms the state-of-the-art methods on the UIL task. Notably, EgoMUIL exhibits superior performance in scenarios involving limited check-in records and follow relations.

Driven by the large amount of spatio-temporal data obtained from location-based social networks, the implementation of cross-domain user linkage, also known as the User Identity Linkage (UIL), has attracted increasing research attentions. While most of the existing UIL works discretize the spatio-temporal sparse data when identifying encountering or co-located events for UIL, user’s distinctive behavior patterns implicit in the “check-in” spatio-temporal data with continuous nature pave the way for enhancing UIL performance. In this paper, we propose an approach dubbedCP-Linkthat exploits user behavior patterns in a continuous way. In CP-Link, the continuous space is divided into irregularly shaped stay regions, and a continuous time-based improved dynamic time warping (IDTW) method is proposed to calculate the similarity. To bridge the gap between the ideal scenario with ample records and the reality with sparse data, we adopt the user-associated location frequent pattern (LFP) model to compensate for the sparse deficiency. Extensive experiments conducted on real-world datasets demonstrate the effectiveness and superiority of CP-Link, which outperforms the state of the arts by more than 20% in terms of the AUC.