Paper Title: "MAT: Effective Link Prediction via Mutual Attention Transformer"
Contributing Students:
- Nguyen Van Quan – 21521333 – Data Science – Primary Author.
- Pham Quang Huy – 21522163 – Data Science – Co-author.
- Tran Quang Dan – 21521917 – Data Science – Co-author.
- Nguyen Kien Bao Thang – 21521432 – Data Science - Co-author.
Supervising Instructor: Mr. Nguyen Hieu Nghia.
Paper Summary:
The DSAA (Data Science and Advanced Analytics) 2023 is part of the DSAA 2023 conference. The challenge aims to explore effective approaches to determine whether a link exists between two Wikipedia pages. Specifically, given a pair of nodes (u, v) along with information about each node, the main objective is to ascertain whether there is a link between the two nodes, where label 1 represents "yes" and label 0 represents "no." The final decision will be based on the effectiveness of the method (scores), performance (time required for training and testing), and the novelty of the proposed method.
By successfully predicting the presence or absence of a link, we contribute to discovering connections and underlying relationships in the Wikipedia network. The results of this task are crucial for various applications such as network reconstruction, recommendation, and understanding the network's evolution.
In this paper, we present the Vanilla Fully Connected (VFC) method and the Mutual Attention Transformer (MAT) method. The VFC method uses only ID information, while the MAT method can utilize both ID and text information of both nodes. Based on the competition results, we can confidently state that both VFC and MAT are effective in addressing the challenges posed in the DSAA Challenge 2023.
The IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA) is a leading data science forum supported by IEEE, ACM, ASA, and CCF. DSAA is recognized by the CORE and CCF conference rankings, ranking in the top 20 for data mining and analysis in Google Metrics. DSAA holds a high position with program chair/host seats, outstanding keynote speakers, interdisciplinary participation, and cross-domain involvement in statistics, computer science, computing, industry, and government, as well as the Next Generation Data Scientist Award.
Further information: https://www.facebook.com/UIT.Fanpage/posts/pfbid037EQsBUzwZujeiANncgpgse9PodYkKjajLVmhZXyrvMpt7VwV1ymuifdWiQjbeJr5l
Written by: Hai Bang
Translated by: Ngoc Diem
