26 citations · 59 across the 9 of their papers we have counts for
10 papers
Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification
Junghoon Kim, Seungyoon Choi, Hyunsung Kim +1
Entities in real-world systems often evolve over time: users shift between information consumption patterns, researchers migrate between fields, and firms transition between financ…
Experimental Analysis and Evaluation of Cohesive Subgraph Discovery
Dahee Kim, Song Kim, Jeongseon Kim +4
Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or rec…
Beyond Trivial Edges: A Fractional Approach to Cohesive Subgraph Detection in Hypergraphs
Hyewon Kim, Woocheol Shin, Dahee Kim +3
Hypergraphs serve as a powerful tool for modeling complex relationships across domains like social networks, transactions, and recommendation systems. The (k,g)-core model effectiv…
Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detection
Hwan Kim, Junghoon Kim, Sungsu Lim
While GNN-based detection methods excel at identifying overt outliers, they often struggle with boundary anomalies -- subtly camouflaged nodes that are difficult to distinguish fro…
Exploring Cohesive Subgraphs in Hypergraphs: The (k,g)-core Approach
Dahee Kim, Junghoon Kim, Sungsu Lim +1
Identifying cohesive subgraphs in hypergraphs is a fundamental problem that has received recent attention in data mining and engineering fields. Existing approaches mainly focus on…
Class Label-aware Graph Anomaly Detection
Junghoon Kim, Yeonjun In, Kanghoon Yoon +2
Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a node is anomalous or not. One common observation we made from previous unsupervised methods is that they…
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
Hwan Kim, Junghoon Kim, Byung Suk Lee +1
Graph anomaly detection has attracted considerable attention from various domain ranging from network security to finance in recent years. Due to the fact that labeling is very cos…
LUEM : Local User Engagement Maximization in Networks
Junghoon Kim, Jungeun Kim, Hyun Ji Jeong +1
Understanding a social network is a fundamental problem in social network analysis because of its numerous applications. Recently, user engagement in networks has received extensiv…
DMCS : Density Modularity based Community Search
Junghoon Kim, Siqiang Luo, Gao Cong +1
Community Search, or finding a connected subgraph (known as a community) containing the given query nodes in a social network, is a fundamental problem. Most of the existing commun…
OCSM : Finding Overlapping Cohesive Subgraphs with Minimum Degree
Junghoon Kim, Sungsu Lim, Jungeun Kim
Cohesive subgraph discovery in a network is one of the fundamental problems and investigated for several decades. In this paper, we propose the Overlapping Cohesive Subgraphs with…