no code implementations • 8 May 2024 • Seoyoung Hong, Jeongwhan Choi, Yeon-Chang Lee, Srijan Kumar, Noseong Park
However, existing methods still have room to improve the trade-offs among accuracy, efficiency, and robustness.
1 code implementation • 25 Feb 2024 • Neng Kai Nigel Neo, Yeon-Chang Lee, Yiqiao Jin, Sang-Wook Kim, Srijan Kumar
The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while ensuring fairness and avoiding biased predictions against individuals from sensitive subgroups such as gender or political leanings.
1 code implementation • 2 Sep 2023 • Min-Jeong Kim, Yeon-Chang Lee, David Y. Kang, Sang-Wook Kim
The proposed approach consists of three modules: (M1) generation of each node's extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings.
1 code implementation • 8 Dec 2022 • Kartik Sharma, Yeon-Chang Lee, Sivagami Nambi, Aditya Salian, Shlok Shah, Sang-Wook Kim, Srijan Kumar
Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions as well as the user-to-user social relations for the task of generating item recommendations to users.
2 code implementations • 14 Nov 2021 • Taeyong Kong, Taeri Kim, Jinsung Jeon, Jeongwhan Choi, Yeon-Chang Lee, Noseong Park, Sang-Wook Kim
To our knowledge, we are the first who design a hybrid method and report the correlation between the graph centrality and the linearity/non-linearity of nodes.