Meta-learning adaptation network for few-shot link prediction in heterogeneous social networks

Link prediction, which aims to predict future or missing links among nodes, is a crucial research problem in social network analysis. A unique few-shot challenge is link prediction on newly emerged link types without sufficient verification information in heterogeneous social networks, such as commodity recommendation on new categories. Most of current approaches for link prediction rely heavily on sufficient verified link samples, and almost ignore the shared knowledge between different link types. Hence, they tend to suffer from data scarcity in heterogeneous social networks and fail to handle newly emerged link types where has no sufficient verified link samples. To overcome this challenge, we propose a model based on meta-learning, called the meta-learning adaptation network (MLAN), which acquires transferable knowledge from historical link types to improve the prediction performance on newly emerged link types. MLAN consists of three main components: a subtask slicer, a meta migrator, and an adaptive predictor. The subtask slicer is responsible for generating community subtasks for the link prediction on historical link types. Subsequently, the meta migrator simultaneously completes multiple community subtasks from different link types to acquire transferable subtask-shared knowledge. Finally, the adaptive predictor employs the parameters of the meta migrator to fuse the subtask-shared knowledge from different community subtasks and learn the task-specific knowledge of newly emerged link types. Experimental results conducted on real-world social media datasets prove that our proposed MLAN outperforms state-of-the-art models in few-shot link prediction in heterogeneous social networks.