Cited By
View all- Tian QZhang WJiao PZhong KWu NPan L(2023)Integrating Higher-Order Features for Structural Role DiscoveryMobile Multimedia Communications10.1007/978-3-031-23902-1_19(244-258)Online publication date: 1-Feb-2023
Attributed network embedding aims to extract latent features of complex networks from structural topology and node attributes. Existing embedding models either use two separate learning processes to capture the complementarity of network topology ...
Role-oriented representation learning extracts structural features by capturing the structural similarities of nodes to generate representations that preserve the structural equivalence, which is crucial for addressing the role discovery task of ...
Network embedding, as a promising way of node representation learning, is capable of supporting various downstream network mining tasks, and has attracted growing research interests recently. Existing approaches mostly focus on learning the low-...
Association for Computing Machinery
New York, NY, United States
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in