Learning low-dimensional representations for Heterogeneous Information Networks (HINs) has drawn increasing attention recently for its effectiveness in real-world applications. Compared with homogeneous networks, HINs are characterized by meta-paths connecting different types of nodes with semantic meanings. Existing methods mainly follow the prototype of independently learning meta-path-based embeddings and integrating them into a unified embedding. However, meta-paths in a HIN are inherently correlated since they reflect different perspectives of the same object. If each meta-path is treated as an isolated semantic data resource and the correlations among them are disregarded, sub-optimality in the both the meta-path based embedding and final embedding will be resulted. To address this issue, we make the first attempt to explicitly model the correlation among meta-paths by proposing Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding (CKD). More specifically, we model the knowledge in each meta-path with two different granularities: regional knowledge and global knowledge. We learn the meta-path-based embeddings by collaboratively distill the knowledge from intra-meta-path and inter-meta-path simultaneously. Experiments conducted on six real-world HIN datasets demonstrates the effectiveness of the CKD method.

User recommendation aims at recommending users with potential interests in the social network. Previous works have mainly focused on the undirected social networks with symmetric relationship such as friendship, whereas recent advances have been made on the asymmetric relationship such as the following and followed by relationship. Among the few existing direction-aware user recommendation methods, the random walk strategy has been widely adopted to extract the asymmetric proximity between users. However, according to our analysis on real-world directed social networks, we argue that the asymmetric proximity captured by existing random walk based methods are insufficient due to the inbalance in-degree and out-degree of nodes. To tackle this challenge, we propose InfoWalk, a novel informative walk strategy to efficiently capture the asymmetric proximity solely based on random walks. By transferring the direction information into the weights of each step, InfoWalk is able to overcome the limitation of edges while simultaneously maintain both the direction and proximity. Based on the asymmetric proximity captured by InfoWalk, we further propose the qualitative (DNE-L) and quantitative (DNE-T) directed network embedding methods, capable of preserving the two properties in the embedding space. Extensive experiments conducted on six real-world benchmark datasets demonstrate the superiority of the proposed DNE model over several state-of-the-art approaches in various tasks.