A survey of frequent subgraph mining algorithms

By Chuntao Jiang, Frans Coenen, Michele A. A. Zito. Knowledge engineering review (Print), 2012

391 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/fd6d101967259f9f8c86f5f5a9871e34d22c63e6

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Citations in the corpus

Multiple Structure-View Learning for Graph Classification (2018, methodology): “Because there is no feature space in the graph domain, this baseline approach first concatenates all the frequent subgraph features discovered from all structure views (i.e., constructing the entire subgraph feature space), and then utilizes the Top-k, IG, gSSC, or gHSIC evaluation criteria to direc”
Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns (2014, methodology): “This idea has been realized by MFS extraction [22] and maximal clique mining [25, 28] in the field of graph mining (reviewed in [11]).”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, background): “Frequent subgraph mining identifies those subgraphs that occur in a corpus more than a given threshold number of times (see [186] for a survey on frequent subgraph mining and see [170–174] for various software).”
Knowledge graph refinement: A survey of approaches and evaluation methods (2016, methodology): “In particular, for many of the methods applied in the works discussed above – such as outlier detection or association rule mining – graph-based variants have been proposed in the literature [2,43].”
A Survey of Sequential Pattern Mining (2017, background): “• Sub-graph mining [110, 59, 11] is another popular pattern mining problem.”
A Reinforcement Learning Environment for Mathematical Reasoning via Program Synthesis (2021, background): “This is known more broadly in the computer science literature as Frequent Subgraph Mining [8].”
Attention Models in Graphs (2018, background): “We do not attempt to survey the vast field of general graph-based methods that do not explicitly apply attention, multiple work have already been done on this with each having a particular focus [Cai et al. 2018; Getoor and Diehl 2015; Jiang et al. 2013].”
Automatic lymphoma classification with sentence subgraph mining from pathology reports. (2014, neutral): “Various graph encodings, enumeration strategies and search pruning policies have been proposed to improve the efficiency of the mining algorithms [44][45].”
Graph of Thoughts: Solving Elaborate Problems with Large Language Models (2023, background): “, understanding phase transitions and critical phenomena), and many others [44, 20, 38, 35, 15].”