Thanh Trung Huynh

TL;DR: This paper proposes an end-to-end, unsupervised entity alignment framework for cross-lingual KGs that fuses different types of information in order to fully exploit the richness of KG data and adopts a late-fusion mechanism to combine all the information together.

TL;DR: NAWAL is proposed, a novel, end-to-end unsupervised embedding-based network alignment framework emphasizing on structural information that significantly outperforms state-of-the-art baselines and demonstrates the robustness against adversarial conditions, such as structural noises and graph size imbalance.

TL;DR: This paper aspires to present a comprehensive examination of machine unlearning’s concepts, scenarios, methods, and applications as a category collection of cutting-edge studies to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine un learning.

TL;DR: This paper proposes a novel end-to-end alignment framework that can leverage different modalities to compare and align network nodes in an efficient way and outperforms state-of-the-art approaches in terms of accuracy on real and synthetic datasets, while being robust against various noise factors.

TL;DR: This work proposes RAN, a representation-based network alignment model that couples both structure and node attribute information and is able to outperform other techniques significantly even on large datasets.