The attention mechanism enables graph neural networks (GNNs) to learn the attention weights between the target node and its one-hop neighbors, thereby improving the performance further. However, most existing G
Figure8presents a graph generated from Table5, illustrating the cumulative number of dead nodes across each round. It vividly reflects the lifetime and energy consumption patterns of different algorithms. From both Table5; Fig.8, it is evident that LEACH-SSA experiences its first dead node later...
: Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation Zequn Sun; Chengming...; 6756: Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion ReasoNet: Learning to Stop Reading in Machine Comprehension读书笔记 multi-hop reasoning阅读理解,通过强...
Incorporating relation paths in neural relation extraction. Empirical Methods in Natural Language Processing. Copenhagen: ACL; 2017. p. 1768–77. 19. Zhang Y, Zheng W, Lin H, Wang J, Yang Z, Dumontier M. Drug-drug interaction extraction via hierarchical rnns on sequence and shortest ...
To this end, we propose a novel model HiAM (Hierarchical Attention based Model) for knowledge graph multi-hop reasoning. HiAM makes use of predecessor paths to provide more accurate semantics for entities and explores the effects of different granularities. Firstly, we extract predecessor paths of ...
Additionally, we employed multi-hop graph convolution layers instead of traditional single-hop methods in GCN to capture complex hierarchical connectivity patterns in brain networks. Our experimental evaluations, conducted on the large, public Human Connectome Project dataset, demonstrate that our proposed ...
Incorporating relation paths in neural relation extraction. Empirical Methods in Natural Language Processing. Copenhagen: ACL; 2017. p. 1768–77. 19. Zhang Y, Zheng W, Lin H, Wang J, Yang Z, Dumontier M. Drug-drug interaction extraction via hierarchical rnns on sequence and shortest ...
Incorporating relation paths in neural relation extraction. Empirical Methods in Natural Language Processing. Copenhagen: ACL; 2017. p. 1768–77. 19. Zhang Y, Zheng W, Lin H, Wang J, Yang Z, Dumontier M. Drug-drug interaction extraction via hierarchical rnns on sequence and shortest ...