# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 2 Related Work
### Network Embedding  
Network embedding [19], or network representation learning, is a method to project nodes in a network to a low dimensional continuous space while preserving network structure and inherent properties. Works in the network embedding mainly consist of two categories, graph embedding (GE) and graph neural network (GNN). Representative works for GE include DeepWalk [20],which generates a corpus on graphs by random walk and then trains a skip-gram model on the corpus. Node2 vec [21] designs a biased random walk procedure to efficiently explore diverse neighborhoods. NetMF [22] is a unified matrix factorization framework for theoretically understanding and improving DeepWalk and LINE. For popular works in GNN, GCN [23] integrated neighbors' feature representations into the node feature representation using convolutional operations. GraphSAGE [24] provides an inductive approach to combine structural information with node features. It learns functional representations instead of direct embeddings for each node, which helps it work inductively on unobserved nodes during training. Due to the ubiquitous network in the real world, network embedding has been paid more and more attention in recent years and used to represent all areas of information. [PERSON] et al. formalize the question of new interactions among its members in a social network as the network embedding problem, and developed approaches to link prediction based on measures for analyzing the proximity of nodes in a network [25]. [PERSON] et al. proposed an augmented relation embedding to map the image space into a semantic manifold, transforming learning a semantic manifold into solving a constrained optimization problem [26]. A Social Network Embedding algorithm achieved the prediction of friend relation and citation relation by extracting the feature representation of a network node through the fusion of node ID and attribute vector [27]. BIGCLAM is proposed as a covering of the community discovery algorithm for the detection of overlapping societies, which generates a probability of each edge in the network model [28]. There has also been lots of effort to devote to the study of recommendation. [PERSON] et al. proposed to use the k-partite adoption graph to characterize various kinds of information in recommendation tasks for addressing recommendation tasks by utilizing the network representation learning techniques [29]. Previous work proposed a new product graph embedding model, which used the network representation learning technology to capture the sequential impact of products by converting historical purchase records into product diagrams [30]. A new collaborative user network embedding method is proposed to extract implicit and reliable social information from user feedback such as ratings or purchases, which transforms feedback into a user-item bipartite graph [31]. From this perspective of network embedding, we can transform App, time, location information into attributed nodes when predicting the next App in the next hour, and construct a graph using these nodes. In the graph, a link represents the relationship of adjacent nodes. For instance, if there is a link between a time node and an App node, it indicates that the user used a certain App at the time. In that way, our problem is actually turned into predicting whether the time node or other node is connected to the App node in the future and sorting the possibilities of establishing a connection. However, although a graph is constructed in the training process, we still utilize some new nodes like new temporal nodes isolated from other vertices in the graph to predict actually. Methods based on the random walk can describe the relationship between App nodes and time nodes or location nodes separately, but they can't solve the problem of new nodes while attributed network embedding can.