TKG: Efficient Mining of Top-K Frequent Subgraphs-Reference-Cited by-同舟云学术

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TKG: Efficient Mining of Top-K Frequent Subgraphs

Published:2019 Issue: Volume: Page:209-226
ISSN:0302-9743
Container-title:Big Data Analytics
language:
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Author:

Fournier-Viger Philippe,Cheng Chao,Lin Jerry Chun-Wei,Yun Unil,Kiran R. Uday

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-37188-3_13

Reference24 articles.

1. Borgwardt, K.M., Ong, C.S., Schönauer, S., Vishwanathan, S.V.N., Smola, A.J., Kriegel, H.P.: Protein function prediction via graph kernels. Bioinformatics 21(Suppl 1), 47–56 (2005)

2. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);Z Cheng,2017

3. Duong, V.T.T., Khan, K.U., Jeong, B.S., Lee, Y.K.: Top-k frequent induced subgraph mining using sampling. In: Proceedings 6th International Conference on Emerging Databases: Technologies, Applications, and Theory (2016)

4. Duong, V.T.T., Khan, K.U., Lee, Y.K.: Top-k frequent induced subgraph mining on a sliding window using sampling. In: Proceedings 11th International Conference on Ubiquitous Information Management and Communication (2017)

5. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);P Fournier-Viger,2016

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3. GraphCCI: Critical Components Identification for Enhancing Security of Cyber-Physical Power Systems;IEEE Transactions on Industrial Cyber-Physical Systems;2024

4. Discovering Frequency Bursting Patterns in Temporal Graphs;2023 IEEE 39th International Conference on Data Engineering (ICDE);2023-04

5. A Frequent Construction Mining Scheme Based on Syntax Tree;Romanian Journal of Information Science and Technology;2023-03-24

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