Mining subgraph coverage patterns from graph transactions.

A Srinivas Reddy, P Krishna Reddy, Anirban Mondal, U Deva Priyakumar

Abstract read

In one paragraph

Article in International journal of data science and analytics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

A Srinivas ReddyKohli Centre on Intelligent Systems, IIIT, Hyderabad, India.ORCID 0000-0003-4212-1460

P Krishna ReddyKohli Centre on Intelligent Systems, IIIT, Hyderabad, India.

Anirban MondalDepartment of Computer Science, Ashoka University, Delhi, India.

U Deva PriyakumarKohli Centre on Intelligent Systems, IIIT, Hyderabad, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns (

Indexed as

Bio-informaticsGraph miningSubgraph coverage patternsSubgraph mining

Identifiers

PMID34873579

DOI10.1007/s41060-021-00292-y

PMCPMC8636072

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.