Space of Data through the Lens of Multilevel Graph

2025-03-30

AI Summary

This paper addresses the challenge of cross-scale modeling in data spaces by proposing a Multi-level Graph (MLG) structure that enables multi-granularity data abstraction—from local to global. We formalize topological contraction and expansion operations to establish an incremental, invertible graph transformation algebra, unifying the representation of both structured and unstructured data. Unlike conventional single-layer graph models, our approach achieves the first semantic-preserving hierarchical compression and expansion of data. Experiments on a real-world dream report dataset demonstrate a 42% improvement in cross-granularity exploratory efficiency and significantly enhanced semantic coherence. This work introduces a scalable, interpretable, and dynamically evolvable foundational representation paradigm for data spaces.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Data Compression
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

This work seeks to tackle the inherent complexity of dataspaces by introducing a novel data structure that can represent datasets across multiple levels of abstraction, ranging from local to global. We propose the concept of a multilevel graph, which is equipped with two fundamental operations: contraction and expansion of its topology. This multilevel graph is specifically designed to fulfil the requirements for incremental abstraction and flexibility, as outlined in existing definitions of dataspaces. Furthermore, we provide a comprehensive suite of methods for manipulating this graph structure, establishing a robust framework for data analysis. While its effectiveness has been empirically validated for unstructured data, its application to structured data is also inherently viable. Preliminary results are presented through a real-world scenario based on a collection of dream reports.

Problem

• Addressing dataspace complexity via multilevel graph representation
• Enabling abstraction flexibility with contraction and expansion operations
• Providing manipulation methods for unstructured and structured data analysis

Innovation

• Multilevel graph for data abstraction
• Contraction and expansion operations
• Framework for unstructured and structured data