Improving Efficiency of Finding Frequent Subgraphs in Graph Stream Using gMatrix Summarization

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KAZEMI MASOUD; Khasteh Seyed Hossein; rokhsati hamidreza

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY Year:2021 | Volume:12 | Issue:45-46 Page(s): 95-114

Download Full-Text

Persian Verion

View:

153

Download:

Cites:

Information Journal Paper

Title

Improving Efficiency of Finding Frequent Subgraphs in Graph Stream Using gMatrix Summarization

Author(s)

KAZEMI MASOUD | Khasteh Seyed Hossein | rokhsati hamidreza | Issue Writer Certificate

Keywords

Graph Stream

Sketch Based Summarization

gMatrix

Hash Functions

Cosine Similarity

Abstract

In many real-world frameworks, dealing with huge domains of nodes and online streaming edges are unavoidable. Transportation systems, IP networks and developed social medias are quintessential examples of such scenarios. One of the most important open problems while dealing with massive Graph Streams are finding frequent sub-graph. There are some approaches such as count-min for storing the frequent nodes, however performing these methods will result in inaccurate modelling of structures based on the main graph. Having said that, gMatrix is one of the recently developed approaches which can fairly save the important properties of the main graph. In this approach, different Hash Functions are utilized to store the basis of streams in the main graph. As a result, having the reverse of the Hash Functions will be extremely useful in calculation of the frequent subgraph. Though gMatrix mainly suffer from two problems. First, they are not really accurate due to high compression rate of the main graph and second, the complexity of returning a query is high. In this thesis, we have presented a new approach based on gMatrix which can reduce the amount of memory usage as well as returning the queries in less amount of time. The main contribution of the introduced approach is to reduce the dependency among the Hash Functions. This will result in less conflicts while creating the gMatrix later. In this study we have used Cosine Similarity in order to estimate the amount of dependency and similarity among Hash Functions. Our experimental results prove the higher performance in terms of algorithm and time complexity.

Cites

No record.

References

No record.

Cite

APA: Copy

KAZEMI, MASOUD, Khasteh, Seyed Hossein, & rokhsati, hamidreza. (2021). Improving Efficiency of Finding Frequent Subgraphs in Graph Stream Using gMatrix Summarization. IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY, 12(45-46 ), 95-114. SID. https://sid.ir/paper/951144/en

Vancouver: Copy

KAZEMI MASOUD, Khasteh Seyed Hossein, rokhsati hamidreza. Improving Efficiency of Finding Frequent Subgraphs in Graph Stream Using gMatrix Summarization. IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY[Internet]. 2021;12(45-46 ):95-114. Available from: https://sid.ir/paper/951144/en

IEEE: Copy

MASOUD KAZEMI, Seyed Hossein Khasteh, and hamidreza rokhsati, “Improving Efficiency of Finding Frequent Subgraphs in Graph Stream Using gMatrix Summarization,” IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY, vol. 12, no. 45-46 , pp. 95–114, 2021, [Online]. Available: https://sid.ir/paper/951144/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops