## 1 Introduction  
With the improvements of living standards, people are more willing to travel and enjoy themselves in places they have not been to. Thus, personalized location-based social networks (LBSNs) and recommendation services emerge and have a rapid development, such as the famous review website Yelp, travel platform Mafengwo, etc. [1]. How to recommend yet-unvisited locations that meet the users' interests has become a popular research topic. Many scholars and application developers invest time and efforts in this task for urban development and the improvement of users' experience. For example, some researchers want to increase the exposure of less popular locations and promote the economy of a city [2, 3]. More and more useful functions are developed in industrial applications that enable users to save decision-making time and avoid travel crowds, and have reward of more pleasant and satisfactory travel experiences. Nowadays, the widespread usage of electronic footprints in mobile positioning technology, such as check-in data in social networks, call detail records or GPS coordinates recorded by mobile phones, enable the location-based services to develop fast. Researchers can exploit the interests of users and improve their personalized location recommender system [4]. To improve recommendation performances, it is significant to understand and exploit user-location latent associations. Recently, many methods aim to explore the implicit representations of users and locations from user-location interactions. For example, location recommendation frameworks GeoMF [5] and GeoMF++ [6] are based on matrix factorization, which introduces geographic features into matrix construction and obtains decomposed geographic features. Various neural networks are also proposed to discover non-linear relationships between users and locations. [PERSON] et al. [7] propose a points of interests (POIs) recommendation model which uses long short-term memory (LSTM) to model check-in data. It also takes the advantage of the multi-layer perceptron (MLP) to integrate social influence and location popularity. A spatio-temporal attention network (STAN) with the dual attention architecture is proposed to predict user's next position [8]. [PERSON] et al. [9] use a combination of convolutional neural network and matrix factorization to obtain latent visiting behaviors. Although these models learn latent embeddings through user-place interactions, they cannot explicitly use the relationships of user-user and place-place, which obviously limits the representation ability. In recent years, due to the advantages of graphs in explicitly presenting entities' interactive relationships, graph-based recommendation algorithms have drawn widespread attention [10; 11]. Explicit interaction structures, such as the adjacency between locations, or friendship between users, can be easily expressed under a graph structure. Now, graph representation learning are widely used to improve recommendations [12; 13], and pursuing the interpretability in recommender system [14]. Nevertheless, there are no research works using graph learning network to solve data sparsity that rooted in recommender system. Therefore, in order to effectively integrate the interactive associations among entities, i.e., users and locations, we propose a graph-based representation model to dig out implicit embedding features of users and locations, respectively, and tackle the data sparsity problem. However, it is not easy to achieve accurate recommending results not only by model ability, but also for serious data sparsity [15; 16], which deeply roots in recommendation systems. To make matters worse, location recommendations have serious temporal and spatial dependence. Compared to millions of locations marked in maps, a user usually leaves footprints only in very few places, actually. For example, the density of user-location check-in data is less than 0.1%, as the research [17] shows.