Masoud Rehyani Hamedani

Research Assistant Professor, Computer Science Department, Hanyang University, Seoul, Korea

Contact

Email

Research Interests

  • Graph Embedding and Feature Representation Learning
  • Deep Learning
  • Recommender Systems
  • Similarity Computation in Information Networks
  • Text Mining and Link Mining
  • Link-based Similarity Measures
  • Classification/Similarity Computation of Android Apps by Machine Learning Techniques

Awards

  • KDD 2025 Best Research Paper Honorable Mention, “SIGEM: A Simple yet Effective Similarity based Graph Embedding Method,” 2025. 07. 24
  • ASK 춘계학술대회 2023 최우수논문상, “Similarity-based methods or conventional ones, which is better for graph embedding?”, 2023. 05. 18
  • 박사학위 우수논문상, “정보 네트워크에서의 유사도 계산,” 2016.
  • 한국정보과학회 제 35회 학생논문경진대회 장려상, “JacSim: 그래프를 위한 정확하고 효율적인 링크 기반 유사도 척도,” 2016. 06. 30.
  • Bronze Medal in the student research competition at ACM SAC 2016, 2016. 04. 08.
  • ACM SAC 2016 Student Travel Award, 2016. 01. 25.
  • 한국정보과학회 제 33회 학생논문경진대회 장려상, “SimCC:내용 정보와 인용 정보를 함께 고려하는 효율적 학술 논문 유사도 계산 방법,” 2014. 06. 27.
  • EDB 2013 Best Poster Award, “Performance Evaluation of Text-based and Linkbased Similarity Measures for Scientific Papers”, 2013. 08. 20.
  • 한국컴퓨터종합학술대회 (KCC 2013) 우수 발표 논문상, “On Computing Similarity Measures of Scientific Papers based on Vector Space Model and Probabilistic Models”, 2013.

Publications

*졸업 이후 실적은 Google Scholar 혹은 Personal Homepage 참고 부탁드립니다.

15 International Conference Papers

  • Seongeun Ryu, Jaesung Hwang, Masoud Reyhani Hamedani, and Sang-Wook Kim, “POPCORN: Popularity-decoupled Interest Matching for Personalized News Recommendation,” In Proc. of the ACM Conference on Knowledge and Information Management 2026 (CIKM 2026), PP. Rome, Italy, Nov 7-11, 2026 (full paper).
  • Dong-Hyuk Seo, Young-Chan Kim, Masoud Hamedani, Doo-Won Lee, and Sang-Wook Kim, “Hours to Milliseconds: Rapid and Accurate Wet Grip Prediction via Graph-Based Semi-Supervised Learning in Tire Development,” In Proc. of the ACM Conference on Knowledge and Information Management 2026 (CIKM 2026), PP. Rome, Italy, Nov 7-11, 2026
  • Masoud Reyhani Hamedani, Jeong-Seok Oh, Seong-Un Cho, and Sang-Wook Kim, “SIGEM: A Simple yet Effective Similarity based Graph Embedding Method,” In Proc. of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’25), pp. Toronto, ON, Canada, August, 2025.
  • Masoud Reyhani Hamedani, Jin-Su Ryu, and Sang-Wook Kim, “Random-walk-based or Similarity-based Methods, Which is Better for Directed Graph Embedding?”, In Proc. of the 11th IEEE International Conference on Big Data and Smart Computing (IEEE BigComp 2024), pp. Bangkok, Thailand, Feb. 18-21, 2024. (accepted to appear). (full paper).
  • Masoud Reyhani Hamedani, Jin-Su Ryu, and Sang-Wook Kim, “ELTRA: An Embedding Method based on Learning-to-Rank to Preserve Asymmetric Information in Directed Graphs,” In Proc. of the 32nd ACM International Conference on Information and Knowledge Management (ACM CIKM 2023), pp. 2116-2125, University of Birmingham and Eastside Rooms, UK, Oct. 21-25, 2023. (full paper).
  • Masoud Reyhani Hamedani, JinSu Ryu and Sang-Wook Kim, “GELTOR: A Graph Embedding Method based on Listwise Learning to Rank”, In Proceedings of the ACM Web Conference 2023 (WWW 2023), pp. 6-16, Texas, USA, Apr. 30 – May 4, 2023. (full paper).
  • Masoud Reyhani Hamedani and Sang-Wook Kim, “Embedding Methods or Link-based Similarity Measures, Which is Better for Link Prediction?”, In Proc. of the IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC 2021), pp. 378-382, Nov. 18-19, 2021. (full paper).
  • Masoud Reyhani Hamedani and Sang-Wook Kim, “AdaSim: A Recursive Similarity Measure in Graphs”, In Proc. of the 30th ACM Int’l Conference on Information and Knowledge Management (ACM CIKM 2021), pp. 1528-1537, ONLINE, Nov. 1-5, 2021. (full paper).
  • Masoud Reyhani Hamedani and Sang-Wook Kim, “Pairwise Normalization in SimRank Variants: Problem, Solution, and Evaluation”, In Proc. of the ACM Symp. on Applied Computing (ACM SAC 2019), pp. 534-541, Limassol, Cyprus, Apr. 8-12, 2019. (full paper).
  • Masoud Reyhani Hamedani and Sang-Wook Kim, “SimCC-AW: A Method to Compute Similarity of Scientific Papers with Automatic Parameter Tuning”, In Proc. of the the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2016), pp. 1005-1008, Pisa, Italy, July 17-21, 2016. (short paper).
  • Masoud Reyhani Hamedani and Sang-Wook Kim, “SimRank and Its Variants in Academic Literature Data: Measures and Evaluation”, In Proc. of the ACM Symp. on Applied Computing (ACM SAC 2016), pp. 1102-1107, Pisa, Italy April 4-8, 2016. (full paper).
  • Masoud Reyhani Hamedani, Sang-Wook Kim, “On Computing Similarity in Academic Literature Data: Methods and Evaluation”, In Proc. of the 1st int’l workshop on big data systems and services (BIDASYS 2014), pp. 403-412, Macau SAR, China, June 16-18, 2014 (full paper).
  • Masoud Reyhani Hamedani, Sang-Chul Lee and Sang-Wook Kim, “On Combining Text-based and Link-based Similarity Measures for Scientific Papers”, In Proc. of ACM Int’l Conf. on Research in Applied Computation Symposium (ACM RACS 2013), pp. 111-115, Montreal, QC, Canada, Oct. 1-4, 2013. (full paper).
  • MasoudReyhani Hamedani, Sang-Chul Lee and Sang-Wook Kim, “On Exploiting Content and Citations Together to Compute Similarity of Scientific Papers”, In Proc. of ACM Int’l Conf. on Information and Knowledge Management (ACM CIKM 2013), pp. 1553-1556, San Francisco, USA, Oct. 27 – Nov. 1, 2013. (short paper).
  • Masoud Reyhani Hamedani, Sang-Chul Lee and Sang-Wook Kim, “Performance Evaluation of Text-based and Linkbased Similarity Measures for Scientific Papers” In Proc. Int’l Conf. on Emerging Databases-Technologies, Applications, and Theory (EDB 2013), pp. 188-191, Jeju Island, Korea, Aug. 19-21, 2013. (short paper).

7 International Journal Papers

  • Masoud Reyhani Hamedani and Sang-Wook Kim, “JacSim*: An Effective and Efficient Solution to The Pairwise Normalization Problem in SimRank”, IEEE Access, Vol. 9, pp. 146038-146049, Oct. 2021.
  • Masoud Reyhani Hamedani and Sang-Wook Kim, “SimAndro-Plus: On Computing Similarity of Android Applications,” Computer Science and Information Systems, Vol. 18(4), pp. 1219-1238, 2021.
  • Masoud Reyhani Hamedani and Sang-Wook Kim, “On Investigating Both Effectiveness and Efficiency of Embedding Methods in Task of Similarity Computation of Nodes in Graphs”, Applied Sciences, Vol. 11, No. 162, pp. 1-29, Jan. 2021.
  • Masoud Reyhani Hamedani, Irfan Ali, Jiwon Hong and Sang-Wook Kim, “TrustRec: An Effective Approach to Exploit Implicit Trust and Distrust Relationships along with Explicit ones for Accurate Recommendations,” Computer Science and Information Systems, Vol. 18, No. 1, pp. 93-114, Jan. 2021.
  • Masoud Reyhani Hamedani, Sang-Wook Kim, “JacSim: An Accurate and Efficient Link-based Similarity Measure in Graphs”, Information Sciences, Vol. 414, pp. 203-224, Nov. 2017.
  • Masoud Reyhani Hamedani, Sang-Wook Kim, and Dong-Jin Kim, “SimCC: A Novel Method to Consider both Content and Citations for Computing Similarity of Scientific Papers”, Information Sciences, Vol. 334, No. 20, pp. 273-292, Mar. 2016.
  • Masoud Reyhani Hamedani and Sang-Wook Kim, “SimCS: Effective Method to Compute Similarity of Scientific Papers”, IEICE Transactions on Information and Systems, Vol. E98-D, No. 12, pp.2328-2332, Dec. 2015.

1 International Patent

  • 김상욱, Reyhani Hamedani Masoud, 대조학습이 필요 없는 자기 지도 학습 기반 그래프 임베딩 방법 및 시스템, 출원번호: PCT/KR2026/007416, 출원일: 2026.06.19.

6 Domestic Conference Papers

  • 정창엽, Masoud Reyhani Hamedani, 김상욱, “지식 그래프 임베딩 방법에 대한 비교 분석: 성능과 시간 효율성 평가,” ASK 2026, pp.611-614, 라카이 샌드파인 (강릉), 2026년 5월 20일–23일
  • 오정석, 마수드, 김상욱 “그래프 임베딩 방법의 성능과 시간 효율성에 따른 비교 분석”, KCC 2025, pp., 제주, 2025년 7월 2-4일
  • 조성운, Masoud Reyhani Hamedani, 김상욱, “그래프 임베딩 방법의 정확도-메모리 트레이드오프 분석”, ASK 2025, pp.412-415, 경북대학교, 2025년 5월 29일- 5월 31일
  • 류진수, 마수드, 김상욱 “방향 그래프 임베딩을 위한 싱글벡터 방법과 더블벡터 방법의 비교 평가”, KDBC 2023, pp.,웨스틴 조선 부산, 2023년 11월 3일 - 11월 4일
  • 류진수, 마수드, 김상욱 “Similarity-based methods or conventional ones, which is better for graph embedding?”, ASK 2023, pp.442-444 , 서울대학교 서울, 2023년 5월 18일 – 5월 20일
  • Masoud Reyhani Hamedani, Sang-ChulLee, Sang-Wook Kim, “On Computing Similarity Measures of Scientific Papers based on Vector Space Model and Probabilistic Models”, 한국정보과학회 Korea Computer Congress 2013 (KCC 2013) pp. 300-302, 여수 디오션리조트, 2013년 6월 26-28일.

1 Domestic Journal Paper

  • 마수드, 김 상욱, “논문 유사도 계산에서 벡터 공간 모델과 확률 모델의 비교 연구”, 한국정보과학회논문지: 컴퓨팅의 실제 및 레터, Vol. 20, No. 3, pp. 186-190, 2014년 3월

6 Domestic Patents

  • 김상욱, 마수드, 류진수, 비대칭 정보를 보존하기 위한 목록 별 순위 학습 기반 방향성 그래프 임베딩 방법 및 시스템, 출원번호: 10-2023-0134799, 출원일: 2023.10.11.
  • 김상욱, 마수드, 그래프의 새로운 재귀 링크 기반 유사성 측정, 출원번호: 10-2021-0173197, 등록번호: 10-2810659, 등록일: 2025.05.16, 출원일: 2021.12.06
  • 김상욱, 마수드, 데이터 마이닝 및 데이터 과학 기술을 적용하여 Android 애플리케이션의 유사성 계산, 등록번호: 10-2697612, 등록일: 2024.08.19., 출원번호: 10-2021-0177476, 출원일: 2021.12.13
  • 김 상욱, 마 수드, 이 상철, 내용 정보 및 참조 정보를 활용하는 문서들 간의 유사도 측정 방법 및 장치, 등록번호: 10-1508849, 등록일: 2015.03.30., (출원번호: 10-2013-0127065, 출원일: 2013. 10. 24.,)
  • 김상욱, 마수드, 오정석, 조성운, 대조학습이 필요 없는 자기 지도 학습 기반 그래프 임베딩 방법 및 시스템, 출원번호: 10-2025-0098191, 출원일: 2025.07.21
  • 김상욱, 마수드, 류진수, 목록 별 순위 학습 기반 그래프 임베딩 방법 및 시스템, 출원번호: 10-2023-0078954, 출원일: 2023.06.20.