scholarly journals Efficient discovery of frequent subgraph patterns in uncertain graph databases

2011 ◽  
Author(s):  
Odysseas Papapetrou ◽  
Ekaterini Ioannou ◽  
Dimitrios Skoutas
Keyword(s):  
Graph Databases ◽  
Uncertain Graph ◽  
Frequent Subgraph

UFreS: A New Technique for Discovering Frequent Subgraph Patterns in Uncertain Graph Databases

2021 ◽  
Author(s):  
Riddho Ridwanul Haque ◽  
Chowdhury Farhan Ahmed ◽  
Md. Samiullah ◽  
Carson K. Leung
Keyword(s):  
New Technique ◽  
Graph Databases ◽  
Uncertain Graph ◽  
Frequent Subgraph ◽  
A New Technique

Frequent Subgraph Mining in Graph Databases Based on MapReduce

2016 ◽  
pp. 464-476 ◽  
Author(s):  
Kai Wang ◽  
Xia Xie ◽  
Hai Jin ◽  
Pingpeng Yuan ◽  
Feng Lu ◽  
...  
Keyword(s):  
Graph Databases ◽  
Frequent Subgraph Mining ◽  
Subgraph Mining ◽  
Frequent Subgraph

Graph similarity search on large uncertain graph databases

2014 ◽  
Vol 24 (2) ◽  
pp. 271-296 ◽  
Author(s):  
Ye Yuan ◽  
Guoren Wang ◽  
Lei Chen ◽  
Haixun Wang
Keyword(s):  
Similarity Search ◽  
Graph Databases ◽  
Uncertain Graph ◽  
Graph Similarity

Mining frequent subgraphs over uncertain graph databases under probabilistic semantics

2012 ◽  
Vol 21 (6) ◽  
pp. 753-777 ◽  
Author(s):  
Jianzhong Li ◽  
Zhaonian Zou ◽  
Hong Gao
Keyword(s):  
Graph Databases ◽  
Uncertain Graph ◽  
Probabilistic Semantics ◽  
Frequent Subgraphs

An efficient mining algorithm for maximal frequent patterns in uncertain graph database

2020 ◽  
Vol 39 (5) ◽  
pp. 7021-7033
Author(s):  
Feng Li
Keyword(s):  
State Of The Art ◽  
Cost Savings ◽  
Search Tree ◽  
Subgraph Isomorphism ◽  
Frequent Patterns ◽  
Graph Database ◽  
Graph Databases ◽  
Uncertain Graph ◽  
Mining Algorithm ◽  
Real World Datasets

Mining maximal frequent patterns is significant in many fields, but the mining efficiency is often low. The bottleneck lies in too many candidate subgraphs and extensive subgraph isomorphism tests. In this paper we propose an efficient mining algorithm. There are two key ideas behind the proposed methods. The first is to divide each edge of every certain graph (converted from equivalent uncertain graph) and build search tree, avoiding too many candidate subgraphs. The second is to search the tree built in the first step in order, avoiding extensive subgraph isomorphism tests. The evaluation of our approach demonstrates the significant cost savings with respect to the state-of-the-art approach not only on the real-world datasets as well as on synthetic uncertain graph databases.

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