Epitomic Variational Graph Autoencoder

Khan, Rayyan Ahmad; Anwaar, Muhammad Umer; Kleinsteuber, Martin

doi:10.1109/icpr48806.2021.9412531

Cited by 7 publications

(1 citation statement)

References 5 publications

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“…There is also an efficient mining algorithm for δ-tolerance closed frequent subgraphs (Takigawa & Mamitsuka, 2011) that combines some pruning techniques to reduce the search space of listing subgraphs. Aiming to avoid the over-pruning problem (Khan et al, 2021), the algorithm (Takigawa & Mamitsuka, 2011) starts with formulating the FSM by using a general enumeration framework (or pattern growth) called a "reverse-search". In Bifet et al (2011), the IncGraphMiner, WinGraphMiner, and AdaGraphMiner algorithms are proposed in network streams for mining closed subgraphs, and are closed subgraph mining algorithms that are incremental, window-based, and adapting, respectively.…”

Section: Mining Closed and Maximal Subgraphsmentioning

confidence: 99%

Subgraph mining in a large graph: A review

Nguyen

Zelinka

Snášel

et al. 2022

WIREs Data Min & Knowl

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Large graphs are often used to simulate and model complex systems in various research and application fields. Because of its importance, frequent subgraph mining (FSM) in single large graphs is a vital issue, and recently, it has attracted numerous researchers, and played an important role in various tasks for both research and application purposes. FSM is aimed at finding all subgraphs whose number of appearances in a large graph is greater than or equal to a given frequency threshold. In most recent applications, the underlying graphs are very large, such as social networks, and therefore algorithms for FSM from a single large graph have been rapidly developed, but all of them have NP-hard (nondeterministic polynomial time) complexity with huge search spaces, and therefore still need a lot of time and memory to restore and process. In this article, we present an overview of problems of FSM, important phases in FSM, main groups of FSM, as well as surveying many modern applied algorithms. This includes many practical applications and is a fundamental premise for many studies in the future.

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