Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

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發(fā)誓放棄

choroid

Product Anomaly Detection on Heterogeneous Graphs with Sparse Labelss, we propose a novel approach for product anomaly detection on heterogeneous graphs. Our approach consists of three key modules: 1) An imbalanced sample strategy that effectively handles class imbalance and high heterogeneity; 2) A label propagation module that tackles the issue of label sparsity;

Phenothiazines

Product Anomaly Detection on Heterogeneous Graphs with Sparse Labelss, we propose a novel approach for product anomaly detection on heterogeneous graphs. Our approach consists of three key modules: 1) An imbalanced sample strategy that effectively handles class imbalance and high heterogeneity; 2) A label propagation module that tackles the issue of label sparsity;

記憶法

Generic and?Scalable Detection of?Risky Transactions Using Density Flows: Applications to?Financial d reduce computation cost. The generic metric and k-Hop density graph detection make our algorithm suitable for the varieties of risky scenarios. Extensive experimental results on several real and synthetic datasets demonstrate the effectiveness of our approach compared to dense subgraph algorithms.

痛恨

Generic and?Scalable Detection of?Risky Transactions Using Density Flows: Applications to?Financial d reduce computation cost. The generic metric and k-Hop density graph detection make our algorithm suitable for the varieties of risky scenarios. Extensive experimental results on several real and synthetic datasets demonstrate the effectiveness of our approach compared to dense subgraph algorithms.

凝乳

Attributed Heterogeneous Graph Embedding with?Meta-graph Attentionlly, the node embeddings under different meta-graphs are fused by considering the importance of meta-graphs. Experimental results on three real datasets show the proposed AHEMA model outperforms the baselines on node classification and node clustering tasks.

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身體萌芽

Automated Multi-scale Contrastive Learning with?Sample-Awareness for?Graph Classificationopology of the input graph and refine neighborhood information. Extensive experiments on eight benchmark datasets demonstrate that our proposed SaMGCL achieves superior graph classification performance compared to the current state-of-the-art approaches.

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