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

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书目名称Artificial Neural Networks and Machine Learning – ICANN 2024影响因子(影响力)




书目名称Artificial Neural Networks and Machine Learning – ICANN 2024影响因子(影响力)学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2024网络公开度




书目名称Artificial Neural Networks and Machine Learning – ICANN 2024网络公开度学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2024被引频次




书目名称Artificial Neural Networks and Machine Learning – ICANN 2024被引频次学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2024年度引用




书目名称Artificial Neural Networks and Machine Learning – ICANN 2024年度引用学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2024读者反馈




书目名称Artificial Neural Networks and Machine Learning – ICANN 2024读者反馈学科排名

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.

邪恶的你

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