- TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs
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Moreover, we have already extracted similarity grids which has two advantages:
- The multi-aspect time-aware code will be understood conveniently.
- The test data (20%) can sufficiently demonstrate the effectiveness of the time-aware module.
Out paper paper "TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs" got accepted in Data Mining and Knowledge Discovery (IF: 3.5, Q1, ISI). Date Accepted: 9-May-2020. Dr. Hosseini is the first and corresponding author in this work.
https://link.springer.com/article/10.1007/s10618-020-00688-7
Out paper paper "SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding" got accepted in IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE). Date Accepted: 12-Mar-2020. Dr. Hosseini is the co-first and corresponding author in this work.
https://ieeexplore.ieee.org/document/9043718
Related articles by the same author:
- Hosseini, Saeid, Hongzhi Yin, Xiaofang Zhou, Shazia Sadiq, Mohammad Reza Kangavari, and Ngai-Man Cheung. "Leveraging multi-aspect time-related influence in location recommendation." World Wide Web (2017): 1-28.
- Hosseini, Saeid, Hongzhi Yin, Meihui Zhang, Yuval Elovici, and Xiaofang Zhou. "Mining Subgraphs From Propagation Networks Through Temporal Dynamic Analysis." In 2018 19th IEEE International Conference on Mobile Data Management (MDM), pp. 66-75. IEEE, 2018.
- Hosseini, Saeid, Hongzhi Yin, Ngai-Man Cheung, Kan Pak Leng, Yuval Elovici, and Xiaofang Zhou. "Exploiting Reshaping Subgraphs from Bilateral Propagation Graphs." In International Conference on Database Systems for Advanced Applications, pp. 342-351. Springer, Cham, 2018.
Dr. Saeid Hosseini, ssaeidhosseini [at] gmail [dot] com