Graph Neural Networks for Digital Pathology by Vincenzo DELLA MEA, Hafsa AKEBLI, Kevin ROITERO

“…The graph learning tasks can be either node-level, edge-level or graph-level. …”
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Fusing multiplex heterogeneous networks using graph attention-aware fusion networks by Ziynet Nesibe Kesimoglu, Serdar Bozdag

“…Using attention-based neighborhood aggregation, GRAF learns the importance of each neighbor per node (called node-level attention) followed by the importance of each network layer (called network layer-level attention). …”
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Condition monitoring of heterogeneous landslide deformation in spatio-temporal domain using advanced graph attention network by Huajin Li, Yu Zhu, Qiang Xu, Ran Tang, Chuanhao Pu, Yusen He

“…This research aims to develop an enhanced spatial-temporal monitoring system capable of capturing these complex deformation patterns. In this study, it presents a novel Graph Attention Network (GAT) framework that integrates multi-scale temporal embeddings, adaptive graph learning, and temporal self-attention mechanisms to simultaneously track localized stability variations and global deformation trends across monitoring points. …”
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A Mobile Computing Framework for Pervasive Adaptive Platforms by Olivier Brousse, Jérémie Guillot, Gilles Sassatelli, Thierry Gil, François Grize, Michel Robert

“…This framework enables the platform to adapt itself to application requirements at high-level while using hardware acceleration at node level. The resulting programming solution has been used to program three collaborative robotic applications in which robots learn tasks and evolve for achieving a better adaptation to their environment.…”
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