NF-GNN:Network Flow Graph Neural Networks for Malware Detection and Classification. (不是CFG) 本文提出CFGExpaliner,是首个针对基于GNN进行恶意软件分类(注意不是检测)的可解释性工作。CFGExplainer在CFG中提取出得到分类结果的关键子图,并对子图内节点进行重要性排序。作者通
一是不同传感器之间有着非常不同的行为,即图中节点的数值和分布差异很大,因此需要考虑如何对传感器,即图中节点进行特征表示;二是GNNs的输入必须是整个图,即包括图中节点的特征表示以及各节点的连接关系,而在本文场景中,各节点之间的关系都是未知的(以往的方法是直接认为各节点之间都存在关系,即使用完全图表征各节点...
To address this problem, we propose a graph neural network-based bearing fault detection (GNNBFD) method. The method first constructs a graph using the similarity between samples; secondly the constructed graph is fed into a graph neural network (GNN) for feature mapping, and the samples output...
a computational RBP binding site prediction framework based on graph convolutional neural networks (GCNs). In contrast to current CNN methods, GraphProt2 offers native support for the encoding of base pair information as well as variable length input, providing increased flexibility and the prediction...
Zhang, Y., Yao, Q., Yue, L.et al.Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network.Nat Comput Sci3, 1023–1033 (2023). https://doi.org/10.1038/s43588-023-00558-4 Download citation ...
推荐系统的发展可分为三个阶段:shallow models -> neural network-based models -> GNN models。其中: shallow models 最早的推荐系统是利用协同过滤(Collaborative Filtering,CF)来计算user和item之间的相似度。后续在此基础上又提出了matrix factorization(MF)、factorization machine等方法。
在这项工作中,我们介绍了一种基于词典的图神经网络lexicon-based graph neural network(LGN),它实现了中文NER作为节点分类任务。该模型打破了RNN的串行化处理结构,通过仔细的连接,字符和单词之间的交互效果更好。词汇知识将相关字符连接起来,以捕获本地成分。同时,设计了一个全局中继节点来捕获远程依赖性和高级特性。LG...
(2021). DeepHunter: A Graph Neural Network Based Approach for Robust Cyber Threat Hunting. In: Garcia-Alfaro, J., Li, S., Poovendran, R., Debar, H., Yung, M. (eds) Security and Privacy in Communication Networks. SecureComm 2021. Lecture Notes of the Institute for Computer Sciences, ...
Graph Neural Networks LabML. https://nn.labml.ai/graphs/index.html (2023).7.LaBonne, M. Graph Attention Networks: Theoretical and Practical Insights https : / / mlabonne . github.io/blog/posts/2022-03-09-graph_attention_net...
回顾了异常检测(Anomaly Detection)、多元时间序列数据模型 (models for multivariate time series data)、图神经网络(Graph neural network)的研究相关工作,并指出其不足。 2.1 异常检测(Anomaly Detection) 目的是检测出偏离大部分数据的异常样本,经典方法包括基于密度的研究方法、基于线性模型的研究方法、基于距离的研究...