Titlebook: Advances in Knowledge Discovery and Data Mining; 25th Pacific-Asia Co Kamal Karlapalem,Hong Cheng,Tanmoy Chakraborty Conference proceedings

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Magnetic Substrates of T1 Relaxation,mances, one of the crucial aspects of KGEs is their capability of inferring relational patterns, such as symmetry, antisymmetry, inversion, and composition. Among the many reasons, the inference capability of embedding models is highly affected by the used loss function. However, most of the existin

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Fast or Turbo Spin Echo Imaging,n in recent years. Most of the recent works employed graph neural networks(GNN) with multiple layers to capture the spatial dependency. However, road junctions with different hop-distance can carry distinct traffic information which should be exploited separately but existing multi-layer GNNs are in

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Magnetic Substrates of T2 Relaxation,ation and link prediction. An important consideration in such applications is the robustness of the embedding algorithms against adversarial attacks, which can be examined by performing perturbation on the original network. An efficient perturbation technique can degrade the performance of network e

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Marzena Kulawska-Didoszak,Gabriel P. Krestinph properties are maximumly preserved. Graph Neural Networks (GNN)-based methods have shown to be effective in dealing with the graph representation learning task. However, most GNN-based methods belong to supervised learning, which depends heavily on the data labels that are difficult to access in

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https://doi.org/10.1007/978-3-540-85689-4or space for each entity and relation. It remains challenging to learn accurate embeddings for complex multi-relational facts. In this paper, we propose a new translation-based embedding method named ATransD-NL to address the following two observations. First, most existing translational methods do

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