Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024影響因子(影響力)




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024影響因子(影響力)學(xué)科排名




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024網(wǎng)絡(luò)公開度




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024被引頻次




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024被引頻次學(xué)科排名




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024年度引用




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024年度引用學(xué)科排名




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024讀者反饋




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024讀者反饋學(xué)科排名

ornithology

3D Lattice Deformation Prediction with?Hierarchical Graph Attention Networkses the deformation predictions to those of physical simulations, achieving high fidelity in modeling real-world phenomena. In contrast to existing GNN architectures built for physical simulation approximation, the CGNN learns realistic folding behavior and lateral movement of individual lattice node

Focus-Words

Beyond Homophily: Attributed Graph Anomaly Detection via?Heterophily-Aware Contrastive Learning Netwusing an unsupervised?edge discriminator. Additionally, a dual-channel encoder is designed?to capture representative node representations from discriminated edges. Extensive experiments on four public benchmark datasets demonstrate that our method is competitive with the most advanced baseline.

邪惡的你

CauchyGCN: Preserving Local Smoothness in?Graph Convolutional Networks via?a?Cauchy-Based Message-Paus strategies, including graph filters, k-hop jumps, and bounded penalties?to tackle this issue, these methods often fall short of explicitly capturing and preserving the local smoothness over the original topology. In this paper, we present CauchyGCN, which enhances preserving local smoothness in a

Cantankerous

amygdala

指耕作

Edged Weisfeiler-Lehman Algorithmddresses one key drawback in many GNNs that do not utilize any edge features of graph data. We evaluated the performance of proposed models using 12 edge-featured benchmark graph datasets and compared them with some state-of-the-art baseline models. Experimental results indicate that our proposed EG

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obsession

Graph-Guided Multi-view Text Classification: Advanced Solutions for?Fast Inference to enhance node sequence information. It integrates features?from multiple views through diverse strategies for both word-level?and text-level fusion. Secondly, to expand the receptive field of nodes, we propose a Remote Feature Extraction Module (RFE) to bridge?the difficult interaction gap betwee

AWRY

Invariant Graph Contrastive Learning for?Mitigating Neighborhood Bias in?Graph Neural Network Based ing the shared variant vectors. Our experiments on three real-world public datasets demonstrate that the IGCL framework significantly outperforms existing baselines, offering a promising solution to overcome the neighborhood bias in GNN-based recommender systems. The source code of our work is avail