A comprehensive and efficient framework for subgraph matching

Cao, Hongtai

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https://hdl.handle.net/2142/127353 Copy

Description

Title

A comprehensive and efficient framework for subgraph matching

Author(s)

Cao, Hongtai

Issue Date

2024-11-20

Director of Research (if dissertation) or Advisor (if thesis)

Chang, Kevin Chen-Chuan

Doctoral Committee Chair(s)

Chang, Kevin Chen-Chuan

Committee Member(s)

• Tong, Hanghang
• Park, Yongjoo
• Cheng, Reynold

Department of Study

Siebel School Comp & Data Sci

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Subgraph Matching
• Heterogeneous Graphs
• Edge Induce
• Vertex Induce
• Homomorphism
• Sharing
• Symmetry Breaking
• Worst-case Optimal Join
• Trie Structural Order
• Clustering
• Equivalence
• Efficiency
• Scalability

Abstract

My doctoral research aims to develop a comprehensive and efficient subgraph matching framework to support graph analytic tasks. In the era of information technology, graphs, consisting of vertices and edges, are widely used to model real-world entities and their relationships, offering new opportunities to understand the world through graph data analysis. To navigate graph data, retrieve interesting structures, and enable knowledge discovery, subgraph matching (SM), which identifies all instances of a given pattern within a graph, is fundamental to graph analysis and consistently attracts intense research interest. Meanwhile, the massive scale and heterogeneous nature of graph data, as well as the complexity of SM problems pose significant challenges to developing efficient algorithms. However, traditional subgraph matching works ignore the above characteristics of real-world data and over-simplify subgraph matching, resulting in ineffective and inefficient solutions. Consequently, graph analysis is extremely time-consuming or impossible on heterogeneous graphs at a large scale. To uncover knowledge hidden behind the data, my research fills in the gap between subgraph matching performance and graph analysis requirements, by directly addressing challenges imposed by graph data characteristics. To consolidate the foundation of graph analysis, my research systematically studies subgraph matching on heterogeneous graphs. First, I analyze the data characteristics in graph analysis and reveal the limitations of existing SM works when they face these characteristics. To develop a comprehensive framework, I focus on large heterogeneous graphs to support different types of vertices and edges, as well as edge directions. To be efficient, I observe the limitations of existing works and identify new concepts that are previously overlooked. Due to the graph heterogeneity, existing algorithms show repetitive computation in checking vertex and edge types, motivating the idea of indexing candidates by heterogeneity characteristics such that repetition can be avoided. Due to the complex edge connections in patterns, existing works perform repetitive matching, indicating a novel concept of candidate dependency to reuse candidates and avoid repetition. As real-world graph analysis often relies on frequent patterns, such patterns are often symmetric and contain subpatterns whose matching results can be shared. This motivates me to improve subgraph matching performance by sharing intermediate results, a novel idea not yet studied in existing works. Second, to systematically develop a subgraph matching framework, I divide the problem into cases of small patterns and large patterns because pattern sizes determine the research direction. In the small pattern case, I design a multilevel sharing solution to improve subgraph matching computation and achieve the goal through systematic multi-objective optimization. In contrast, to handle the complexity imposed by large patterns, I develop another solution using heuristics that scales better with pattern sizes. Finally, my work is systematically evaluated through experiments, and it outperforms state-of-the-art algorithms, showing its effectiveness and efficiency in subgraph matching.

Graduation Semester

2024-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/127353

Copyright and License Information

Copyright 2024 Hongtai Cao

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