Data Mining

Featured Articles - Volume 1, Number 1

• Adaptive Graph Neural Networks for Dynamic Anomaly Detection in Evolving Networks with Concept Drift

  Ling Liu

  Network anomaly detection faces serious challenges when dealing with evolving systems. Traditional methods fail when the underlying data distribution shifts over time. This paper presents an adaptive graph neural network framework...

  Keywords: Graph Neural Networks, Anomaly Detection, Concept Drift, Adaptive Learning

• Causal-Aware Temporal Graph Mining for Early Risk Prediction in Financial Transaction Networks

  Yanling Liu

  Financial transaction networks evolve constantly, making risk detection a moving target. This paper introduces a novel approach that combines causal inference with temporal graph mining to predict financial risks...

  Keywords: Temporal Graph Mining, Causal Inference, Financial Risk Detection, Transaction Networks

• Privacy-Preserving Federated Learning Framework for Cross-Domain Sequential Pattern Mining with Differential Privacy Guarantees

  Yifei Yin

  Sequential pattern mining across different domains faces significant privacy challenges when organizations need to collaborate without sharing raw data. This paper presents a federated learning framework...

  Keywords: Sequential Pattern Mining, Federated Learning, Differential Privacy, Cross-Domain Learning

• Continuous Learning Architecture for Real-Time Fraud Detection in Blockchain-Based Financial Systems

  Xingwang Pi

  Financial fraud in blockchain systems presents unique challenges that traditional detection methods struggle to address. This paper introduces a continuous learning architecture specifically designed...

  Keywords: Blockchain Security, Continuous Learning, Fraud Detection, Real-Time Analytics

• Quantum-Inspired Optimization for Large-Scale Subgraph Pattern Mining with Probabilistic Constraints

  Yanlun Chen

  Mining meaningful patterns from massive graphs presents unique computational challenges that traditional algorithms struggle to handle efficiently. This paper introduces a novel quantum-inspired optimization framework...

  Keywords: Quantum-Inspired Optimization, Subgraph Pattern Mining, Probabilistic Constraints, Large-Scale Graph Analysis