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

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

scholarly journals K-Graph: Knowledgeable Graph for Text Documents

2021 ◽  
Vol 51 (1) ◽  
pp. 73-89
Author(s):  
Varsha Mittal ◽  
Durgaprasad Gangodkar ◽  
Bhaskar Pant
Keyword(s):  
Structural Information ◽  
Low Complexity ◽  
Semantic Knowledge ◽  
Access Time ◽  
Graph Databases ◽  
Text Documents ◽  
Memory Overhead ◽  
Frequent Subgraphs ◽  
Real World Datasets ◽  
Different Levels

Abstract Graph databases are applied in many applications, including science and business, due to their low-complexity, low-overheads, and lower time-complexity. The graph-based storage offers the advantage of capturing the semantic and structural information rather than simply using the Bag-of-Words technique. An approach called Knowledgeable graphs (K-Graph) is proposed to capture semantic knowledge. Documents are stored using graph nodes. Thanks to weighted subgraphs, the frequent subgraphs are extracted and stored in the Fast Embedding Referral Table (FERT). The table is maintained at different levels according to the headings and subheadings of the documents. It reduces the memory overhead, retrieval, and access time of the subgraph needed. The authors propose an approach that will reduce the data redundancy to a larger extent. With real-world datasets, K-graph’s performance and power usage are threefold greater than the current methods. Ninety-nine per cent accuracy demonstrates the robustness of the proposed algorithm.

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

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.

scholarly journals Risk-Averse Matchings over Uncertain Graph Databases

2019 ◽  
pp. 71-87 ◽  
Author(s):  
Charalampos E. Tsourakakis ◽  
Shreyas Sekar ◽  
Johnson Lam ◽  
Liu Yang
Keyword(s):  
Risk Averse ◽  
Graph Databases ◽  
Uncertain Graph

Algorithms of the Most Reliable Maximum Flow on Uncertain Graph

2012 ◽  
Vol 35 (11) ◽  
pp. 2371
Author(s):  
Wei CAI ◽  
Bai-Li ZHANG ◽  
Jian-Hua LV
Keyword(s):  
Maximum Flow ◽  
Uncertain Graph
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