10 citations · 20 across the 6 of their papers we have counts for
6 papers
Scalable attribute-aware network embedding with locality
Weiyi Liu, Zhining Liu, Toyotaro Suzumura +1
Adding attributes for nodes to network embedding helps to improve the ability of the learned joint representation to depict features from topology and attributes simultaneously. Re…
Principled Multilayer Network Embedding
Weiyi Liu, Pin-Yu Chen, Sailung Yeung +2
Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network d…
Learning Graph Topological Features via GAN
Weiyi Liu, Hal Cooper, Min Hwan Oh +4
Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological…
Can GAN Learn Topological Features of a Graph?
Weiyi Liu, Pin-Yu Chen, Hal Cooper +3
This paper is first-line research expanding GANs into graph topology analysis. By leveraging the hierarchical connectivity structure of a graph, we have demonstrated that generativ…
A Novel Methodologyof Router-To-ASMapping inspired by Community Discovery
Weiyi Liu, Qing Jiang, Gaolei Fei +2
In the last decade many works has been done on the Internet topology at router or autonomous system (AS) level. As routers is the essential composition of ASes while ASes dominate…
Mining Essential Relationships under Multiplex Networks
Liu Weiyi, Chen Lingli, Hu Guangmin
In big data times, massive datasets often carry different relationships among the same group of nodes, analyzing on these heterogeneous relationships may give us a window to peek t…