A Survey on Role-Oriented Network Embedding

Recently, Network Embedding (NE) has become one of the most attractive research topics in machine learning and data mining. NE approaches have achieved promising performance various graph mining tasks including link prediction node clustering classification. A wide variety methods focus on proximity networks, they learn community-oriented embedding for each node, where corresponding representations are similar if two nodes closer to other network. Meanwhile, there is another type structural similarity, i.e., role-based which completely different from complementary proximity. In order preserve problem role-oriented raised. However, compared NE, only a few proposed recently. Although less explored, considering importance roles analyzing networks many applications that can shed light on, it necessary timely provide comprehensive overview existing methods. this review, we first clarify differences between network embedding. Afterward, propose general framework understanding two-level categorization better classify Then, select some representative according briefly introduce them by discussing their motivation, development, differences. Moreover, conduct experiments empirically evaluate these role-related classification (role discovery), top-k similarity search, visualization using widely used synthetic real-world datasets. Finally, further discuss trend perspective point out potential future directions.