## 2 Related Work  
The relevant studies can be divided into two categories, location promotion and network representation learning in graphs. We will first describe both, and discuss their comparisons with our work. **Location Promotion in LBSNs. ** Location promotion is to find a set of users as seeding promotors such that the number of influenced users visiting the targeted POI can be maximized. There are several typical methods in this research direction. [PERSON] et al. [10] developed the model named LoCaTe to quantify the influence of a location and a user by modeling user-location interaction patterns, and categorical and temporal semantics between users and locations. [PERSON] et al. [12] aim at modeling how users interact with each other via their friendships, and accordingly devise a user proximity score for recommending companions for an activity. [PERSON] et al. [13] incorporate the geographical region information into the identification of influential users, and develop a new LBSN influence propagation method to achieve the goal. POI2 Vec [14] is designed to predict users who will visit a given location within a future period. POI sequential transition and user preference are used to learn the representation of POI so that potential visitors can be effectively predicted. [PERSON] et al. [11] also aim to select a set of seeding users and expect them to attract most other users to visit a given location. Its novelty is the modeling of user mobility from check-in records so that the location-aware propagation probabilities can be better derived. The main difference between our work and existing studies in this category is two-fold. First, past methods on location promotion utilize historical check-in records to find the most influential users who can attract the most other users to visit a targeted location. The influence of users is estimated based on historical data. However, these methods cannot predict whether the identified influential users can truly visit the location in the future. In other words, these methods are not to predict the _future_ influence of users for location promotion. Second, although the method proposed by [PERSON] et al. [11] can effectively predict whether a user will visit the targeted location in the future, it is not to identify the influential users who can better serve as seeding promotors, attracting more users to visit the targeted location. In short, our work is the first work that predicts future user influence that can bring more other users to visit the targeted location in the future. Note that the main difference between the proposed propagators and the so-called influencers in existing studies is that previous influencers are identified based on only the social network structure via diffusion simulation. The identified influencers cannot be ensured to have the same influence in the future. That said, the mining of influencers in the literature does not consider the predictability. We argue that for real applications, the predictability of whether influencers can truly activate more users should be taken into account. We treat the influencers with predictability as propagators to be detected in our work. **Network Representation Learning in Graphs. ** Network representation learning, also known as node embedding learning, aims to generate a low-dimensional vector for each node in a graph. The goal is to preserve the neighborhood proximity between nodes in the embedding vectors of nodes. Two nodes that share similar neighbors are supposed to have similar vectors in the embedding space. There are three typical studies in network representation learning, node2 vec [18], LINE [19], and GraphGAN [20]. The node2 vec proposes to sample the neighbors of a particular node via a hybrid BFS and DFS mechanism, and apply the Skip-gram model [21] to generate the embedding vectors. LINE aims to preserve both the local and global network structures, i.e., the first- and second-order pairwise proximities, for nodes in the embedding space.