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Finding Path Motifs in Large Temporal Graphs Using Algebraic Fingerprints.

Suhas Thejaswi1, Aristides Gionis1,2, Juho Lauri3

  • 1Department of Computer Science, Aalto University, Aalto, Finland.

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Summary
This summary is machine-generated.

We developed a new framework for finding color patterns in temporal graphs, enabling efficient data analysis for applications like tour recommendations and fraud detection. This method scales to massive datasets, offering practical solutions for complex pattern-matching challenges.

Keywords:
algebraic algorithmsconstrained multilinear sievingpattern detectiontemporal pathstemporal patterns

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Area of Science:

  • Computer Science
  • Graph Theory
  • Algorithms

Background:

  • Temporal graphs represent dynamic networks where connections change over time.
  • Pattern detection in these graphs is crucial for various real-world applications.
  • Existing methods struggle with the scale and complexity of temporal graph pattern matching.

Purpose of the Study:

  • To address pattern-detection problems in vertex-colored temporal graphs.
  • To develop an efficient framework for searching temporal paths with specific color multisets.
  • To establish complexity results and provide a scalable algorithmic solution.

Main Methods:

  • An algebraic-algorithmic framework based on constrained multilinear sieving was designed.
  • The framework handles queries for temporal paths containing specified multisets of vertex colors.
  • Complexity results for these pattern-detection problems were established.

Main Results:

  • The proposed solution demonstrates scalability to massive graphs (billions of edges).
  • Efficient performance is achieved even for complex queries (e.g., 10 colors on millions of edges).
  • The implementation shows practical edge-linear scalability and high optimization.

Conclusions:

  • The developed framework offers a significant advancement in temporal graph pattern detection.
  • The approach provides efficient and scalable solutions for complex pattern-matching tasks.
  • Publicly available implementation facilitates practical application in diverse domains.