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This article explores how black‑market cheating in marketing campaigns and e‑commerce is detected using graph‑based techniques such as same‑person mining, label propagation, Fraudar, and GCN models, and discusses future directions like multimodal data fusion and real‑time dynamic graph computation.
This article presents a comprehensive overview of recent graph neural network research, covering adversarial representation learning for network embedding, block‑model guided GCN, enhanced class‑discriminative GNNs, self‑supervised contrastive GNNs, experimental results, and conclusions, highlighting their significance in real‑world applications.
This article presents how graph neural network models such as GCN and SCGCN are employed to detect and recall cheating groups in user‑invitation (master‑apprentice) activity scenarios, addressing the lack of relational features and low sample purity, and demonstrates significant recall improvements through multi‑graph fusion techniques.
This article provides an introductory overview of graph neural networks, explaining their motivation, basic concepts, and detailing classic models such as GCN, GraphSAGE, GAT, Graph Auto‑Encoder, and DiffPool, along with their advantages, limitations, and experimental results on various benchmark datasets.
Graph Neural Networks (GNNs) have become a research hotspot, and this article provides an intuitive overview of classic GNN models such as GCN, GraphSAGE, GAT, graph auto‑encoders, and DiffPool, discussing their architectures, advantages, limitations, and experimental results across various benchmark datasets.
Precise testing technology uses static code scanning and dynamic tracing to build a Neo4j call‑graph, automatically recommends test scopes and cases via diff analysis and weighted relationships—including call‑count, module, text similarity, and GCN—thereby improving test adequacy, cutting regression cycles, and dramatically reducing test execution time.
This article presents a comprehensive study on detecting malicious webpages using heterogeneous graph structures and Graph Convolutional Networks, detailing background challenges, technical approaches, model iterations, optimization techniques for large‑scale deployment, experimental results, and directions for future research.
This article presents a comprehensive study on detecting malicious webpages by constructing heterogeneous graphs from URL redirection and textual features, applying Graph Convolutional Networks and Cluster‑Text‑GCN models, detailing optimization techniques for large‑scale deployment, and outlining future research directions.
The talk presents an overview of graph neural network development, explains 极验's graph modeling research and evolution, and details offline and real‑time GCN solutions, including self‑supervised training, large‑scale handling, and performance comparisons, highlighting practical applications in fraud detection and risk control.
The paper presents a graph convolutional network approach that leverages multi‑task learning and spectral graph convolutions to forecast shared‑bike inflow, outflow, and demand gaps across a city’s non‑Euclidean parking network, demonstrating improved accuracy over traditional time‑series baselines while noting scalability and directional graph limitations.
This article explains how various graph algorithms—including connected components, label propagation, Louvain community detection, and Graph Convolutional Networks—are built on large‑scale user‑behavior graphs using Spark GraphX to detect and mitigate advertising fraud, detailing methodology, implementation, and experimental results.
This article describes how graph theory and graph convolutional neural networks are leveraged to model user‑IP relationships, detect fraudulent advertising clusters, and improve detection accuracy and recall through a combination of unsupervised graph algorithms and supervised GCN training in a large‑scale ad‑anti‑fraud system.
This paper introduces a method for video multi-label classification that incorporates label correlation features using graph convolutional networks, significantly improving classification performance.
This article examines the challenges of deepening Graph Convolutional Networks (GCN), introduces ResGCN, DenseGCN, and skip‑neighbor designs to enable deeper architectures, presents experimental results showing improved performance with 28‑layer models, and outlines future research directions.
The article explains how the heat diffusion equation and the Laplacian operator on graphs provide a physical intuition for Graph Convolutional Networks, showing the equivalence between continuous‑space Fourier analysis and discrete‑space message passing, and linking these concepts to semi‑supervised learning and GraphSAGE implementations.