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

  • ASK 춘계학술대회 2023 최우수논문상
  • Best Ph.D. dissertation awardee (Graduate School of Hanyang University)
  • Best paper award (Korean Institute of Information Scientists and Engineers)
  • Bronze medal, Student Research Competition of ACM SAC
  • Best paper award (Korean Institute of Information Scientists and Engineers)
  • Best short paper award (International Conference on Emerging Database, EDB)
  • Best short paper award (Korean Computer Congress, KCC)

Publications

12 International Conference Papers

  • 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 (full paper). (accepted to appear).
  • 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., University of Birmingham and Eastside Rooms, UK, Oct. 21-25, 2023 (full paper).
  • Masoud Reyhani Hamedani, Jin-Su Ryu, and Sang-Wook Kim. “GELTOR: A Graph Embedding Method based on Listwise Learning to Rank”. In Proc. of the ACM Web Conference 2023 (ACM WWW 2023), pp. 6–16, Texas, USA, April 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 Rehyani 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-AT: 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 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, “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).
  • 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, 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 2021), Vol. 18, No. 1, pp. 93-114, Jan. 2021.
  • Masoud Reyhani Hamedani and Sang-Wook Kim, “SimAndro-Plus: On Computing Similarity of Android Applications,” (Computer Science and Information Systems 2021), 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, 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 2015), Vol. E98-D, No. 12, pp.2328-2332, Dec. 2015.

3 Domestic Conference Paper

  • 류진수, 마수드, 김상욱 “방향 그래프 임베딩을 위한 싱글벡터 방법과 더블벡터 방법의 비교 평가”, KDBC 2023, pp.,웨스틴 조선 부산, 2023년 11월 3-4일
  • 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-Chul Lee, 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

5 Domestic Patents

  • 김상욱, 마수드, 데이터 마이닝 및 데이터 과학 기술을 적용하여 Android 애플리케이션의 유사성 계산, 출원번호: 10-2021-0177476
  • 김상욱, 마수드, 류진수, 비대칭 정보를 보존하기 위한 목록 별 순위 학습 기반 방향성 그래프 임베딩 방법 및 시스템: 10-2023-0134799
  • 김상욱, 마수드, 류진수, 목록 별 순위 학습 기반 그래프 임베딩 방법 및 시스템, 출원번호: 10-2023-0078954
  • 김상욱, 마수드, 데이터 마이닝 및 데이터 과학 기술을 적용하여 Android 애플리케이션의 유사성 계산, 출원번호: 10-2021-0177476,
  • 김상욱, 마수드, 그래프의 새로운 재귀 링크 기반 유사성 측정, 출원번호: 10-2021-0173197