Network Embedding and Its Applications

Apart from the attached attributes of entities, the relationships among entities are also an important perspective that reveals the topological structure of entities in a complex system. A network (or graph) with nodes representing entities and links indicating relationships, has been widely used in sociology, biology, chemistry, medicine, the Internet, etc. However, traditional machine learning and data mining algorithms, designed for the entities with attributes (i.e., data points in a vector space), cannot effectively and/or efficiently utilize the topological information of a network formed by relationships among entities. To fill this gap, Network Embedding (NE) is proposed to embed a network into a low dimensional vector space while preserving some topologies and/or properties, so that the resulting embeddings can facilitate various downstream machine learning and data mining tasks. Although there have been many successful NE methods, most of them are designed for embedding static plain networks. In fact, real-world networks often come with one or more additional properties such as node attributes and dynamic changes. The central research question of this thesis is ``where and how can we apply NE for more realistic scenarios?''. To this end, we propose three novel NE methods, each of which is for addressing the new challenges resulting from one type of more realistic networks. Besides, we also discuss the applications of NE with the focus to the drug-target interaction prediction problem.