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A survey on graph neural networks for intrusion detection systems: methods, trends and challenges
Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the
increasing sophistication of cyber attack methods, traditional detection approaches are …
increasing sophistication of cyber attack methods, traditional detection approaches are …
Graph neural networks in IoT: A survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
Graphmae: Self-supervised masked graph autoencoders
Self-supervised learning (SSL) has been extensively explored in recent years. Particularly,
generative SSL has seen emerging success in natural language processing and other …
generative SSL has seen emerging success in natural language processing and other …
Graphmae2: A decoding-enhanced masked self-supervised graph learner
Graph self-supervised learning (SSL), including contrastive and generative approaches,
offers great potential to address the fundamental challenge of label scarcity in real-world …
offers great potential to address the fundamental challenge of label scarcity in real-world …
Simple and asymmetric graph contrastive learning without augmentations
Abstract Graph Contrastive Learning (GCL) has shown superior performance in
representation learning in graph-structured data. Despite their success, most existing GCL …
representation learning in graph-structured data. Despite their success, most existing GCL …
Gad-nr: Graph anomaly detection via neighborhood reconstruction
Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within
graphs, finding applications in network security, fraud detection, social media spam …
graphs, finding applications in network security, fraud detection, social media spam …
Decoupled self-supervised learning for graphs
This paper studies the problem of conducting self-supervised learning for node
representation learning on graphs. Most existing self-supervised learning methods assume …
representation learning on graphs. Most existing self-supervised learning methods assume …
Ai-accelerated discovery of altermagnetic materials
Altermagnetism, a new magnetic phase, has been theoretically proposed and
experimentally verified to be distinct from ferromagnetism and antiferromagnetism. Although …
experimentally verified to be distinct from ferromagnetism and antiferromagnetism. Although …
Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications—A comprehensive review
In the last decade, there has been a significant surge of interest in machine learning,
primarily driven by advancements in deep learning (DL). DL has emerged as a powerful …
primarily driven by advancements in deep learning (DL). DL has emerged as a powerful …
ProtoMGAE: prototype-aware masked graph auto-encoder for graph representation learning
Y Zheng, C Jia - ACM Transactions on Knowledge Discovery from Data, 2024 - dl.acm.org
Graph self-supervised representation learning has gained considerable attention and
demonstrated remarkable efficacy in extracting meaningful representations from graphs …
demonstrated remarkable efficacy in extracting meaningful representations from graphs …