Sungsu Lim

Associate Professor

Chungbuk National University

South Korea

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About

Dr. Sungsu Lim is an Associate Professor at Chungnam National University in South Korea. His research primarily focuses on graph anomaly detection, traffic forecasting, and social network analysis. He has contributed to various fields through his recent publications, including the development of machine learning techniques for image retrieval and time series pattern prediction.

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Collaborators (3)

Junghwan Kim

Yonsei University

SOUTH KOREA

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Hyungtae Cho

school of energy materials and chemical engineering korea university of technology and education (koreatech) cheonan 31

SOUTH KOREA

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Dongil Kim

Associate Professor

Ewha Womans University

SOUTH KOREA

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Sungsu Lim

Research Interests

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About

Articles (22)

Label-based graph augmentation with metapath for graph anomaly detection

Sequence-aware adaptive graph convolutional recurrent networks for traffic forecasting

Bridges in social networks: current status and challenges

LLM-powered scene graph representation learning for image retrieval via visual triplet-based graph transformation

Dynamic Periodic Event Graphs for multivariate time series pattern prediction

Succinct Link Transformation-Based Overlapping Community Detection Framework for Social Network Analysis

Vessel trajectory classification via transfer learning with Deep Convolutional Neural Networks

Experimental analysis and evaluation of cohesive subgraph discovery

Machine Learning-Based Anomaly Detection on Seawater Temperature Data with Oversampling

User tendency-based rating scaling in online trading networks

A novel graph-based missing values imputation method for industrial lubricant data

MIGTNet: Metapath Instance-based Graph Transformation Network for heterogeneous graph embedding

A framework for environmental production of textile dyeing process using novel exhaustion-rate meter and multi-layer perceptron-based prediction model

Effective and efficient core computation in signed networks

mr2vec: Multiple role-based social network embedding

Graph-Based Anomaly Detection of Ship Movements Using CCTV Videos

LUEM : Local User Engagement Maximization in Networks

OCSM : Finding overlapping cohesive subgraphs with minimum degree

Link Pruning for Community Detection in Social Networks

Graph Anomaly Detection With Graph Neural Networks: Current Status and Challenges

LEHAN: Link-Feature Enhanced Heterogeneous Graph Attention Network

SiReN: Sign-Aware Recommendation Using Graph Neural Networks

Collaborators (3)

Junghwan Kim

Hyungtae Cho

Dongil Kim

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