## 2 Related Work
GraphGAN applies generative adversarial networks (GAN) for network representation learning. Given a node in a network, the generative model is to fit its connectivity distribution over all nodes, and produces fake embeddings to make the discriminative model wrong. The discriminative model aims to determine whether the generated node embedding is true or artificial, i.e., generated by the generative model. In our experiments, we will compare the proposed methods with these three models of network presentation learning. The differences between the proposed LBSN2 vec and existing graph embedding methods consist of three parts. First, our LBSN2 vec is able to effectively exploit two graphs, i.e., the social network and the check-in network, to generate the node embeddings by jointly considering social neighborhood and check-in neighborhood. However, the existing method takes advantage of only the social network. Being able to incorporate multi-model information in a united framework is the key to LBSN2 vec. Second, in LBSN2 vec, we introduce a penalization mechanism to avoid the random walk traversing the same node so that nodes with higher distinguishability can be sampled. Such a penalization cannot be achieved by existing methods. Third, in the experiments, we have found that when having our extensive design from existing methods, LBSN2 vec and its variant PLBSN2 vec can improve the performance on both tasks of TPD and TCD. Recent Advances in LBSNRecent advances in mining location-based social networks focus on recommendation point-of-interests for users. [PERSON] et al. [22] classify immediate and distant social relationships based on check-in data for better POI recommendation. A hierarchical attention mechanism (HAM) [23], which effectively selects the more representative LBSN data instances for training, is devised to perform efficient personalized POI recommendation. DSPR [24] further leverages user preferences and real-time demand, along with an attention-based recurrent neural network, for POI recommendation. While labeled data are scarce in practice, a semi-supervised learning framework HisRect [25] is developed to take advantage of unlabeled data to recommend the next POIs. SACRA is a self-attentive prospective customer recommendation framework [26] that makes comparisons between users' check-ins by adversarial training so that POI recommendation can be more robust. [PERSON] et al. [27] further combine metric learning and few-shot learning to fully utilize user check-ins and better learn the semantic matching between users and POIs. GCLR [28] is a graph-based geographical latent representation model to capture non-linear geographical influences based on the user-POI check-in graph. Although these are successful proposals for point-of-interest recommendation for users, none of them discuss how to apply advanced neural network and representation learning methods for discovering influential propagators and predicting potential customers. We believe existing methods can accurately predict the next POIs. Since our problem is significantly different from POI recommendation, i.e., estimating the future influence of users regarding a targeted location in our work, a novelty of our work can be the first to adopt representation learning for predicting propagators and customers in location-based social networks.