Anomaly Detection in Attributed Networks via Residual Modeling
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Solution Overview
Problem
Existing anomaly detection methods in attributed networks rely on prior knowledge of anomaly properties, which is often not available, and struggle to handle heterogeneous data sources and interconnected instances, making it difficult to identify anomalies effectively.
Innovation Solution
A processor-configured learning framework that models residuals of attribute information and its coherence with network information to detect anomalies, using a principled approach that reconstructs attribute information and integrates network modeling to rank anomalies based on residual values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing anomaly detection methods use prior knowledge of anomaly properties, then detection can be performed in specific contexts, but the methods cannot handle various types of anomalies in real-world attributed networks where prior knowledge is not available
Solution Approach 1:
The system performs self-service by automatically learning anomaly detection capabilities from the data itself without requiring external prior knowledge. The neural network learns to identify anomalies by processing attributed network data and generating anomaly scores based on learned patterns, making the system adaptable to various anomaly types while maintaining detection reliability
Solution Approach 2:
The system changes parameters by transforming the input data through a neural network that learns optimal parameter representations. The network adjusts internal parameters (weights and biases) during training to capture different anomaly types, enabling the system to handle diverse anomalies without predefined knowledge while maintaining accurate detection
2Adaptability or versatility
If attributed networks use heterogeneous data representations, then rich network representation is achieved, but challenges arise for anomaly detection due to heterogeneity of two data representations
Solution Approach 1:
The system merges heterogeneous data representations by integrating attribute data and network structure data into a unified neural network model. The network processes both types of data simultaneously through shared layers, combining their representations to enable effective anomaly detection while managing the complexity of handling heterogeneous inputs
Solution Approach 2:
The neural network acts as an intermediary that transforms heterogeneous data representations into a common feature space. The network layers serve as mediators that process different data types (attributes and network structures) and convert them into unified representations that can be jointly analyzed for anomaly detection
3Measurement precision
If a principled learning framework models residuals of attribute information and coherence with network information, then anomaly detection performance is improved, but the framework complexity increases compared to baseline methods
Solution Approach 1:
The framework segments the anomaly detection task into distinct components: attribute residual modeling and network coherence modeling. These segmented functions are implemented as separate but integrated neural network modules, allowing the system to achieve high detection precision through specialized processing while managing overall framework complexity through modular architecture
Data Source
AI summary
A processor is configured with a learning framework to characterize the residuals of attribute information and its coherence with network information for improved anomaly detection.