TL;DR
Experiments show that the SOP-GNN model achieves state-of-the-art performance in node classification tasks, validating its effectiveness in capturing spatial topological information and improving classification accuracy.
A multi-layer graph convolution feature mining model with adaptive neighbor aggregation, aiming at the problems of incomplete feature extraction and weak topological dependency perception in traditional GNNs for complex correlation datasets is proposed.
This work proposes a novel Adaptive Dual-level Collaborative GNN associated with an adaptive dual-level collaborative mechanism, and shows that the ADC-GNN can inject the learned high-level information back into the node level, forming a closed-loop, bidirectional optimization process.
CNNG, a novel hybrid deep learning model combining Convolutional Neural Networks and Graph Attention Networks for predicting node influence, is proposed, which achieves the best average Kendall’s Tau coefficient and the highest average Monotonicity Index over ten networks.
A diffusion-enhanced inductive link prediction framework that combines Graph Diffusion Convolution (GDC), structural node descriptors, and neighborhood aggregation from GraphSAGE is proposed that achieves higher accuracy than the other models on the benchmark datasets.
Graph Neural Networks (GNNs) have exhibited extraordinary performance in data mining. The success depends on the message passing mechanism, which is consistent with the homophily assumption that adjacent nodes share similar behaviors. However, the connected nodes in real-world graphs often show dissimilar behaviors, wh...
Results show that mixed-distribution training can improve structural transfer in GNN-based centrality approximation, while identifying closeness centrality's sensitivity to topology as an open challenge.
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