Data Intelligence Lab

데이터 인텔리전스 연구실

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Data Intelligence Lab focuses on collecting, analyzing, and utilizing diverse and complex real-world data effectively. Our major research topics include data mining, graph machine learning, network science, and recommendation systems. Our goal is to develop "accurate and trustworthy data mining methodologies," which can help solve real-world problems and enhance the quality of life in society.

Major research field

Data Mining, Graph Machine Learning, Network Science, Recommender Systems

Desired field of research

Accurate, Robust, and Fair Graph Machine Learning and Its Applications

Research Keywords and Topics

그래프 표현 학습, 신뢰할 수 있는 그래프 마이닝, 추천 시스템, 그래프 + X
Graph Representation Learning, Trustworthy Graph Mining, Recommender Systems, Graph + X

Research Publications

- The ACM Web Conference (WWW), Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding, Hyunsik Yoo / Yeon-Chang Lee / Kijung Shin / Sang-Wook Kim, (2023.05)
- ACM International Conference on Information & Knowledge Management (CIKM), MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation, {Taeri Kim* / Yeon-Chang Lee*} / Kijung Shin / Sang-Wook Kim, (2022.10)
- ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Look Before You Leap: Confirming Edge Signs in Random Walk with Restart for Personalized Node Ranking in Signed Networks, {Wonchang Lee* / Yeon-Chang Lee*} / Dongwon Lee / Sang-Wook Kim, (2021.07)


- 사용자와의 거리를 기반으로 한 아이템 추천 방법 및 장치, 이연창, 박준하, 김상욱, (2023.08)
- 적대적 학습에 기반한 부호가 있는 네트워크 임베딩 방법 및 장치, 이연창, 서나윤, 김상욱, (2021.10)


  • EE. 정보/통신
  • EE01. 정보이론
  • EE0108. 인공지능