Learning Node Representations via Sketching the Generative Process With Events Benefits Link Prediction on Heterogeneous Networks

The Heterogeneous Information Network (HIN) stands out as a prominent tool for depicting interactions in real-world systems. Recently, representation learning on HINs has attracted significant attention, as the structured and compact output embeddings offer great convenience for network analysis and graph machine learning tasks. While existing HIN representation learning methods excel in supervised training or direct proximity reconstruction, yielding satisfactory performance in tasks like node clustering and classification, they often overlook the critical HIN generative process characterized by numerous events. As a result, these methods fail to preserve the higher-order interactions among the nodes and predict potential links in HINs. To address these limitations, we propose a Contrastive Learning method via Events on Heterogeneous Information Networks (CLEH). CLEH delineates the generative process from the local structure (nodes) to the higher-order structure (events) in HINs. We design a novel event-level contrastive learning procedure, endowing representations with the capability to capture higher-order relations among nodes. Moreover, CLEH leverages a normalizing flow model as the encoder to enhance the expressiveness of embeddings. Experimental results on HIN datasets demonstrate the significant superiority of CLEH in link prediction compared to popular baselines.