Examinando por Autor "Zulaika Zurimendi, Unai"

Mostrando 1 - 5 de 5
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    Analysing centralities for organisational role inference in online social networks
    (Elsevier Ltd, 2021-03) Sánchez Corcuera, Rubén; Bilbao Jayo, Aritz; Zulaika Zurimendi, Unai; Almeida, Aitor
    The intensive use of Online Social Networks (OSN) nowadays has made users expose more information without realising it. Malicious users or marketing agencies are now able to infer information that is not published on OSNs by using data from targets friends to use for their benefit. In this paper, the authors present a generalisable method capable of deducing the roles of employees of an organisation using their Twitter relationships and the features of the graph from their organisation. The authors also conduct an extensive analysis of the node centralities to study their roles in the inference of the different classes proposed. Derived from the experiments and the ablation study conducted to the centralities, the authors conclude that the latent features of the graph along with the directed relationships perform better than previously proposed methods when classifying the role of the employees of an organisation. Additionally, to evaluate the method, the authors also contribute with a new dataset consisting of three directed graphs (one for each organisation) representing the relationships between the employees obtained from Twitter.
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    LWP-WL: link weight prediction based on CNNs and the Weisfeiler–Lehman algorithm
    (Elsevier Ltd, 2022-05-01) Zulaika Zurimendi, Unai; Sánchez Corcuera, Rubén; Almeida, Aitor; López de Ipiña González de Artaza, Diego
    We present a new technique for link weight prediction, the Link Weight Prediction Weisfeiler–Lehman (LWP-WL) method that learns from graph structure features and link relationship patterns. Inspired by the Weisfeiler–Lehman Neural Machine, LWP-WL extracts an enclosing subgraph for the target link and applies a graph labelling algorithm for weighted graphs to provide an ordered subgraph adjacency matrix into a neural network. The neural network contains a Convolutional Neural Network in the first layer that applies special filters adapted to the input graph representation. An extensive evaluation is provided that demonstrates an improvement over the state-of-the-art methods in several weighted graphs. Furthermore, we conduct an ablation study to show how adding different features to our approach improves our technique's performance. Finally, we also perform a study on the complexity and scalability of our algorithm. Unlike other approaches, LWP-WL does not rely on a specific graph heuristic and can perform well in different kinds of graphs
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    Predictive assessment of eating disorder risk and recovery: uncovering the effectiveness of questionnaires and influencing characteristics
    (Elsevier B.V., 2025) Pikatza Huerga, Amaia; Las Hayas Rodríguez, Carlota; Zulaika Zurimendi, Unai; Almeida, Aitor
    This study aims to assess the predictive capabilities of various questionnaires in determining the risk of Eating Disorders (ED) and predicting the level of recovery among individuals. Employing machine learning models and diverse datasets, the research focuses on understanding the effectiveness of different questionnaires in providing insights into ED symptoms and recovery outcomes. Additionally, the study seeks to identify the characteristics that significantly influence the recovery process. The investigation aims to contribute valuable information to enhance the diagnostic and monitoring tools used in the field of mental health, particularly concerning ED
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    Regularized online tensor factorization for sparse knowledge graph embeddings
    (Springer Science and Business Media Deutschland GmbH, 2023) Zulaika Zurimendi, Unai; Almeida, Aitor; López de Ipiña González de Artaza, Diego
    Knowledge Graphs represent real-world facts and are used in several applications; however, they are often incomplete and have many missing facts. Link prediction is the task of completing these missing facts from existing ones. Embedding models based on Tensor Factorization attain state-of-the-art results in link prediction. Nevertheless, the embeddings they produce can not be easily interpreted. Inspired by previous work on word embeddings, we propose inducing sparsity in the bilinear tensor factorization model, RESCAL, to build interpretable Knowledge Graph embeddings. To overcome the difficulties that stochastic gradient descent has when producing sparse solutions, we add l1 regularization to the learning objective by using the generalized Regularized Dual Averaging online optimization algorithm. The proposed method substantially improves the interpretability of the learned embeddings while maintaining competitive performance in the standard metrics
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    Towards more interpretable graphs and Knowledge Graph algorithms
    (Universidad de Deusto, 2022-12-13) Zulaika Zurimendi, Unai; López de Ipiña González de Artaza, Diego; Almeida, Aitor; Facultad de Ingeniería; Programa de Doctorado en Ingeniería para la Sociedad de la Información y Desarrollo Sostenible por la Universidad de Deusto
    The increase in the amount of data generated by today’s technologies has led to the creation of large graphs and Knowledge Graphs that contain millions of facts about people, things and places in the world. Grounded on those large data stores, many Machine Learning models have been proposed to achieve different tasks, such as predicting new links or weights. Nevertheless, one of the main challenges of those models is their lack of interpretability. Commonly known as “black boxes”, Machine Learning models are usually not understandable to humans. This lack of interpretability becomes even a more severe problem for Knowledge graph-related applications, including healthcare systems, chatbots, or public service management tools where end-users require an understanding of the feedback given by the models. In this thesis, we present methods to increase the interpretability of graphs and Knowledge Graphs based Machine Learning models. We follow a taxonomy grounded on the output result obtained by the proposed methods. Each of the different methods is suitable for particular use cases and scenarios, and can help end-users in different manners. Precisely, we provide an interpretable link weight prediction method based on the Weisfeiler-Lehman graph colouring technique. Additionally, we present an adaption of the Regularized Dual Averaging optimization method for Knowledge Graphs to obtain interpretable representations in link prediction models. Lastly, we introduce the use of Influence Functions for Knowledge Graph link prediction models to acquire the most im- important training facts for a given prediction. Through experiments in link weight prediction and link prediction, we show that our methods can successfully increase the interpretability of the machine learning models of graphs and Knowledge Graphs while maintaining competition with state-of-the-art methods in terms of performance.