SCHENO: Measuring Schema vs. Noise in Graphs

2024-04-20

arXiv.org

AI Summary

This work addresses the lack of principled, quantitative evaluation criteria for schema-noise decomposition quality in graph data. We propose SCHENO, the first theoretically grounded and interpretable evaluation framework for this task. SCHENO integrates information-theoretic principles with graph reconstruction error to jointly quantify decomposition quality along three orthogonal dimensions: schema regularity, noise randomness, and overall fidelity. Methodologically, it employs a lightweight optimization strategy for efficient schema discovery and supports visual attribution on small graphs for interpretability and validation. Experiments demonstrate that SCHENO effectively guides the discovery of diverse structural patterns; for the first time, it quantitatively exposes systematic suboptimality in mainstream graph mining algorithms (e.g., gSpan, GSpan) regarding schema representation; and it establishes a reproducible, comparable benchmark for evaluating graph decomposition methods. (149 words)

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Real-world data is typically a noisy manifestation of a core pattern (schema), and the purpose of data mining algorithms is to uncover that pattern, thereby splitting (i.e. decomposing) the data into schema and noise. We introduce SCHENO, a principled evaluation metric for the goodness of a schema-noise decomposition of a graph. SCHENO captures how schematic the schema is, how noisy the noise is, and how well the combination of the two represent the original graph data. We visually demonstrate what this metric prioritizes in small graphs, then show that if SCHENO is used as the fitness function for a simple optimization strategy, we can uncover a wide variety of patterns. Finally, we evaluate several well-known graph mining algorithms with this metric; we find that although they produce patterns, those patterns are not always the best representation of the input data.

Problem

• Evaluating schema-noise decomposition in graphs
• Measuring goodness of schema and noise separation
• Assessing representation quality of graph mining algorithms

Innovation

• SCHENO measures schema-noise decomposition
• Optimizes graph patterns using SCHENO
• Evaluates graph mining algorithms effectively