Quaternion Graph Neural Networks

Recently, graph neural networks (GNNs) become a principal research direction to learn low-dimensional continuous embeddings of nodes and graphs to predict node and graph labels, respectively. However, Euclidean embeddings have high distortion when using GNNs to model complex graphs such as social networks. Furthermore, existing GNNs are not very efficient with the high number of model parameters when increasing the number of hidden layers. Therefore, we move beyond the Euclidean space to a hyper-complex vector space to improve graph representation quality and reduce the number of model parameters. To this end, we propose quaternion graph neural networks (QGNN) to generalize GCNs within the Quaternion space to learn quaternion embeddings for nodes and graphs. The Quaternion space, a hyper-complex vector space, provides highly meaningful computations through Hamilton product compared to the Euclidean and complex vector spaces. As a result, our QGNN can reduce the model size up to four times and enhance learning better graph representations. Experimental results show that the proposed QGNN produces state-of-the-art accuracies on a range of well-known benchmark datasets for three downstream tasks, including graph classification, semi-supervised node classification, and text (node) classification. Our code is available at: https://github.com/daiquocnguyen/QGNNComment: The extended abstract has been accepted to NeurIPS 2020 Workshop on Differential Geometry meets Deep Learning (DiffGeo4DL). The code in Pytorch and Tensorflow is available at: https://github.com/daiquocnguyen/QGN

Two-view Graph Neural Networks for Knowledge Graph Completion

We present an effective GNN-based knowledge graph embedding model, named WGE, to capture entity- and relation-focused graph structures. In particular, given the knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. In addition, WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a GNN-based architecture to better learn vector representations of entities and relations from these two single entity- and relation-focused graphs. WGE feeds the learned entity and relation representations into a weighted score function to return the triple scores for knowledge graph completion. Experimental results show that WGE outperforms competitive baselines, obtaining state-of-the-art performances on seven benchmark datasets for knowledge graph completion.Comment: 13 pages; 3 tables; 3 figure

What is reflection? A conceptual analysis of major definitions and a proposal of a five-component definition and model

La réflexion est considérée comme un élément significatif de la pédagogie et de la pratique médicales sans qu’il n’existe de consensus sur sa définition ou sur sa modélisation. Comme la réflexion prend concurremment plusieurs sens, elle est difficile à opérationnaliser. Une définition et un modèle standard sont requis afin d’améliorer le développement d’applications pratiques de la réflexion. Dans ce mémoire, nous identifions, explorons et analysons thématiquement les conceptualisations les plus influentes de la réflexion, et développons de nouveaux modèle et définition. La réflexion est définie comme le processus de s’engager (le « soi » (S)) dans des interactions attentives, critiques, exploratoires et itératives (ACEI) avec ses pensées et ses actions (PA), leurs cadres conceptuels sous-jacents (CC), en visant à les changer et en examinant le changement lui-même (VC). Notre modèle conceptuel comprend les cinq composantes internes de la réflexion et les éléments extrinsèques qui l’influencent.Although reflection is considered a significant component of medical education and practice, the literature does not provide a consensual definition or model for it. Because reflection has taken on multiple meanings, it remains difficult to operationalize. A standard definition and model are needed to improve the development of practical applications of reflection. In this master’s thesis, we identify, explore and thematically analyze the most influential conceptualizations of reflection, and develop a new theory-informed and unified definition and model of reflection. Reflection is defined as the process of engaging the self (S) in attentive, critical, exploratory and iterative (ACEI) interactions with one’s thoughts and actions (TA), and their underlying conceptual frame (CF), with a view to changing them and a view on the change itself (VC). Our conceptual model consists of the five defining core components, supplemented with the extrinsic elements that influence reflection