Global ETD Search
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Showing 1 to 5 of 5 for “"Heterogeneous Graphs"”.
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MACHINE LEARNING FOR TEMPORAL HETEROGENEOUS GRAPHS: PREDICTIVE METHODS, INTERPRETABILITY AND APPLICATIONS.
… and interpreting predictive methods for temporal heterogeneous graphs by integrating modeling and tools from network science and graph deep learning. We introduce discrete-time graph learning architectures based on Graph Neural Networks (GNNs) and linear scoring functions tailored for forecasting …
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A comprehensive and efficient framework for subgraph matching
… tasks. In the era of information technology, graphs, consisting of vertices and edges, are widely used to model real-world entities and their relationships, offering new opportunities to understand the world through graph data analysis. To navigate graph data, retrieve interesting structures, …
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Graph Neural Networks for Health-Aware Food and Multi-Criteria Recommendation Systems
… Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA) and the Multiview Graph Dual Attention and Contrastive Learning for Multi-Criteria Recommender Systems (D-MGAC). The first framework, HFRS-DA, addresses the challenge of effectively integrating heterogeneous …
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Structure-aware Deep Learning
… of data for training. Due to their complexity, graphs remain an under-utilized resource in this regard. Many approaches cannot incorporate them due to being fully structurally unaware or not suited to the specific flavour of graphs encountered in some domains. This disconnect is sub-optimal from …
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Consistent community detection in uni-layer and multi-layer networks
… For community detection in uni and multi-layer graphs, we take three approaches - (1) based on statistical random graph models, (2) based on maximizing quality functions, e.g., the modularity score and (3) based on spectral and matrix factorization methods. In Chapter 2 we consider two random …