10 citations · 16 across the 4 of their papers we have counts for
5 papers
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs
Dongjin Lee, Juho Lee, Kijung Shin
Real-world graphs are dynamic, constantly evolving with new interactions, such as financial transactions in financial networks. Temporal Graph Neural Networks (TGNNs) have been dev…
Graphlets over Time: A New Lens for Temporal Network Analysis
Deukryeol Yoon, Dongjin Lee, Minyoung Choe +1
Graphs are widely used for modeling various types of interactions, such as email communications and online discussions. Many of such real-world graphs are temporal, and specificall…
I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs
Dongjin Lee, Kijung Shin
Although machine learning on hypergraphs has attracted considerable attention, most of the works have focused on (semi-)supervised learning, which may cause heavy labeling costs an…
SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams
Taehyung Kwon, Inkyu Park, Dongjin Lee +1
Consider traffic data (i.e., triplets in the form of source-destination-timestamp) that grow over time. Tensors (i.e., multi-dimensional arrays) with a time mode are widely used fo…
Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers
Dongjin Lee, Kijung Shin
Consider multiple seasonal time series being collected in real-time, in the form of a tensor stream. Real-world tensor streams often include missing entries (e.g., due to network d…