2 citations · 4 across the 3 of their papers we have counts for
4 papers
SLeDGe: Semi-Supervised Learning on Data Streams with Graph Structure Learning
Heechan Moon, Kijung Shin
Semi-supervised learning (SSL) on data streams is challenging due to the continuous evolution of high-volume data and the scarcity of labels. Existing methods are limited in levera…
Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts
Jongha Lee, Taehyung Kwon, Heechan Moon +1
The problem of predicting node properties (e.g., node classes) in graphs has received significant attention due to its broad range of applications. Graphs from real-world datasets…
Four-set Hypergraphlets for Characterization of Directed Hypergraphs
Heechan Moon, Hyunju Kim, Sunwoo Kim +1
A directed hypergraph, which consists of nodes and hyperarcs, is a higher-order data structure that naturally models directional group interactions (e.g., chemical reactions of mol…
BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning
Jihoon Ko, Shinhwan Kang, Taehyung Kwon +2
Continual Learning (CL) is the process of learning ceaselessly a sequence of tasks. Most existing CL methods deal with independent data (e.g., images and text) for which many bench…