【论文解读 CIKM 2018 | GEM】Heterogeneous Graph Neural Networks for Malicious Account Detection

时间 2020-12-30

标签科研繁體版

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论文链接：Heterogeneous Graph Neural Networks for Malicious Account Detection 来源：CIKM 2018（CCF-B 数据库，数据挖掘与内容检索) 官方介绍：https://zhuanlan.zhihu.com/p/48243724 1.摘要本文提出GEM模型，是一个异质图神经网络方法，用于在支付宝中检测恶意账户。本文的方法受连

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1. 【论文解读 CIKM 2018 | GEM】Heterogeneous Graph Neural Networks for Malicious Account Detection
2. 【论文解读 CIKM 2018 | JUST】Are Meta-Paths Necessary? Revisiting Heterogeneous Graph Embeddings
3. Heterogeneous Graph Neural Networks for Extractive Document Summarization
4. ACL2020 Heterogeneous Graph Neural Networks for Extractive Document Summarization
5. 论文阅读：Deep Neural Networks for Object Detection
6. 【论文笔记】Heterogeneous Graph Neural Networks for Extractive Document Summarization
7. 【论文解读 KDD 2019 | MEIRec】Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
8. 论文解读：Graph Convolutional Networks for Text Classifification
9. 科恩论文阅读：Semantic-Aware Neural Networks for Binary Code Similarity Detection
10. 《Heterogeneous Graph Attention Network》论文解读

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