10 citations · 17 across the 11 of their papers we have counts for
11 papers
Centrality-Based Node Feature Augmentation for Robust Network Alignment
Jin-Duk Park, Cong Tran, Won-Yong Shin +1
Network alignment (NA) is the task of discovering node correspondences across multiple networks. Although NA methods have achieved remarkable success in a myriad of scenarios, thei…
A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty
Yu Hou, Cong Tran, Ming Li +1
In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy co…
Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation
Jin-Duk Park, Cong Tran, Won-Yong Shin +1
Network alignment (NA) is the task of discovering node correspondences across different networks. Although NA methods have achieved remarkable success in a myriad of scenarios, the…
META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks
Yu Hou, Cong Tran, Won-Yong Shin
The discovery of community structures in social networks has gained considerable attention as a fundamental problem for various network analysis tasks. However, due to privacy conc…
On the Power of Gradual Network Alignment Using Dual-Perception Similarities
Jin-Duk Park, Cong Tran, Won-Yong Shin +1
Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fac…
IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology
Cong Tran, Won-Yong Shin, Andreas Spitz
Since the structure of complex networks is often unknown, we may identify the most influential seed nodes by exploring only a part of the underlying network, given a small budget f…