Frequent SubGraph Mining Algorithms: Framework, Classification, Analysis, Comparisons

Velampalli, Sirisha; Jonnalagedda, Venkatesh

doi:10.1007/978-981-10-3223-3_31

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…It is now possible to represent a large amount of data with a graph. There are several application fields based on graph mining and some of the fields are as follows: (1). Chemical informatics, (2).…”

Section: Introductionmentioning

confidence: 99%

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Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

Sharmila¹,

Devi²

2023

Computer Systems Science and Engineering

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Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today's on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve the issues and provide safe big data transmission, the proposed technique also combines blockchain with a consensus algorithm architecture. Security of the big data is ensured by dividing the BC network into distinct networks, each with a restricted number of allowed entities, data kept in the cloud gate server, and data analysis in the blockchain. The consensus algorithm is crucial for maintaining the speed, performance and security of the blockchain. Then Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Finally, the graph index refinement process is undertaken to improve the query results. Concerning query error, fuzzy logic is used to refine the index of the graph dynamically. The proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems, and the combination of blockchain and subgraph provides a secure communication platform, according to the findings.

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Section: Introductionmentioning

confidence: 99%

“…Web analysis and (7). Social networks [1][2][3][4]. In this field, graph index construction has been paid great attention, which aims to speed up the query processing [5].…”

Section: Introductionmentioning

confidence: 99%

Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

Sharmila¹,

Devi²

2023

Computer Systems Science and Engineering

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“…Today, a lot of data can be represented in the form of graph. There are several application fields based on the graph mining and some of the fields are as follows: (1). Chemical informatics, (2).…”

Section: Introductionmentioning

confidence: 99%

BTLA-LSDG: Blockchain-Based Triune Layered Architecture for Authenticated Subgraph Query Search in Large-Scale Dynamic Graphs

Sharmila¹,

Devi²

2023

IETE Journal of Research

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Over the past era, subgraph mining from a large collection of graph database is a crucial problem. Existing works on subgraph mining is based on the threshold value, which returns similar graphs for a query graph. However, the number of graphs in the answer set for the same threshold can vary in terms of structure and context. In addition, scalability is another big problem due to insufficient storage.Further, it can suffer from security issues. In a distributed environment, since more queries come from different users, it is possible for the attackers to access the graph. These three problems highly exist in the current subgraph mining. To address this downside, our proposed work introduces aBlockchain-based Triune Layered Architecture for authenticated query search in the large scale dynamic graphs. The two fold process is handled in BTLA-LSDG: graph indexing and authenticated query search (query processing), which are implemented in triune layers (Data Generation Layer, Data Storage Layer, and Service Layer). Initially, data owners are authenticated to blockchain using Four-Q-Curve algorithm. The graph index is constructed by data owners and merged graph index is constructed by service providers. Based on the uploaded graph index, hash index is constructed using SHA-3. The hash index is fed into blockchain and form Dendrimer -Fractal Index. On the other hand, data user submits query with authentication. For every authenticated query, the four fold process is handled. Firstly, Multi-Constraint based Belief Entropy function is used for feature sets computation for a given query. Then Dual Similarity based MapReduce helps inmapping and reducing the relevant subgraphs with the use of optimal feature sets. Thirdly, Recurrent Neural Network (RNN)is used for subgraph isomorphic testing. Finally, graph index refinement process is undertaken to improve the query results. For this, query error is computed for each user's query at the end of retrieval. With respect to query error, fuzzy logic is used to refine the index of graph dynamically. This experiment is implemented using Hadoop environment and the results show better efficiency in terms of Scalability, Security and Storage. Further, it is tested with Precision, Recall, F-measure, Accuracy, Error Rate, Query Response Time and Positive Results.

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“…Graph analysis (Karwa et al, 2014) is the premise for many applications, including ones considering social networks (Chakraborty et al, 2018;Ding et al, 2019), bioinformatics (Hill et al, 2012;Mrzic et al, 2018), chemical compounds (Oldenhof et al, 2020), terrorist networks (Basu et al, 2018;McMillan et al, 2020), and so on. FSM forms the basis for graph clustering (Dhillon et al, 2005), graph-based anomaly detection (Paudel et al, 2018;Pourhabibi et al, 2020), and graph classification (Fournier-Viger et al, 2020;Fox et al, 2020;Velampalli & Jonnalagedda, 2018).…”

Section: Introductionmentioning

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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Frequent SubGraph Mining Algorithms: Framework, Classification, Analysis, Comparisons

Cited by 8 publications

References 13 publications

Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

BTLA-LSDG: Blockchain-Based Triune Layered Architecture for Authenticated Subgraph Query Search in Large-Scale Dynamic Graphs

Subgraph mining in a large graph: A review

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