graphkit-learn: A Python Library for Graph Kernels Based on Linear Patterns

Autor: Paul Honeine, Benoit Gaüzère, Linlin Jia
Přispěvatelé: Equipe Apprentissage (DocApp - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), ANR-18-CE23-0014,APi,Apprivoiser la Pré-image(2018)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Theoretical computer science
Computational complexity theory
Computer science
Computation
02 engineering and technology
Python Implementation
[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
01 natural sciences
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
[INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY]
Artificial Intelligence
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
0103 physical sciences
Trie
0202 electrical engineering
electronic engineering
information engineering
Relevance (information retrieval)
010306 general physics
computer.programming_language
Statement (computer science)
Linear Patterns
Model selection
Graph Kernels
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Python (programming language)
Graph
Signal Processing
Benchmark (computing)
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
computer
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Software
Zdroj: Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2021, 143, pp.113-121. ⟨10.1016/j.patrec.2021.01.003⟩
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2021.01.003⟩
Popis: International audience; This paper presents graphkit-learn, the first Python library for efficient computation of graph kernels based on linear patterns, able to address various types of graphs. Graph kernels based on linear patterns are thoroughly implemented, each with specific computing methods, as well as two wellknown graph kernels based on non-linear patterns for comparative analysis. Since computational complexity is an Achilles' heel of graph kernels, we provide several strategies to address this critical issue, including parallelization, the trie data structure, and the FCSP method that we extend to other kernels and edge comparison. All proposed strategies save orders of magnitudes of computing time and memory usage. Moreover, all the graph kernels can be simply computed with a single Python statement, thus are appealing to researchers and practitioners. For the convenience of use, an advanced model selection procedure is provided for both regression and classification problems. Experiments on synthesized datasets and 11 real-world benchmark datasets show the relevance of the proposed library.
Databáze: OpenAIRE
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