scholarly journals A STUDY ON GRAPH MINING ALGORITHMS TO DISCOVER FREQUENT SUBGRAPH PATTERNS FROM EXACT GRAPH DATA AND UNCERTAIN GRAPH DATABASE

2018 ◽  
Vol 9 (3) ◽  
Keyword(s):  
Graph Mining ◽  
Graph Database ◽  
Uncertain Graph ◽  
Graph Data ◽  
Frequent Subgraph ◽  
Mining Algorithms

scholarly journals A Graph Mining Approach for Ranking and Discovering the Interesting Frequent Subgraph Patterns

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Saif Ur Rehman ◽  
Kexing Liu ◽  
Tariq Ali ◽  
Asif Nawaz ◽  
Simon James Fong
Keyword(s):  
Graph Mining ◽  
A Priori ◽  
Research Work ◽  
User Involvement ◽  
Threshold Value ◽  
Research Field ◽  
Frequent Subgraph Mining ◽  
Graph Data ◽  
Frequent Subgraph ◽  
Pattern Growth

AbstractGraph mining is a well-established research field, and lately it has drawn in considerable research communities. It allows to process, analyze, and discover significant knowledge from graph data. In graph mining, one of the most challenging tasks is frequent subgraph mining (FSM). FSM consists of applying the data mining algorithms to extract interesting, unexpected, and useful graph patterns from the graphs. FSM has been applied to many domains, such as graphical data management and knowledge discovery, social network analysis, bioinformatics, and security. In this context, a large number of techniques have been suggested to deal with the graph data. These techniques can be classed into two primary categories: (i) a priori-based FSM approaches and (ii) pattern growth-based FSM approaches. In both of these categories, an extensive research work is available. However, FSM approaches are facing some challenges, including enormous numbers of frequent subgraph patterns (FSPs); no suitable mechanism for applying ranking at the appropriate level during the discovery process of the FSPs; extraction of repetitive and duplicate FSPs; user involvement in supplying the support threshold value; large number of subgraph candidate generation. Thus, the aim of this research is to make do with the challenges of enormous FSPs, avoid duplicate discovery of FSPs, and use the ranking for such patterns. Therefore, to address these challenges a new FSM framework A RAnked Frequent pattern-growth Framework (A-RAFF) is suggested. Consequently, A-RAFF provides an efficacious answer to these challenges through the initiation of a new ranking measure called FSP-Rank. The proposed ranking measure FSP-Rank effectively reduced the duplicate and enormous frequent patterns. The effectiveness of the techniques proposed in this study is validated by extensive experimental analysis using different benchmark and synthetic graph datasets. Our experiments have consistently demonstrated the promising empirical results, thus confirming the superiority and practical feasibility of the proposed FSM framework.

scholarly journals Evaluation of Proposed Algorithm with Preceding GMT for Fraudulence Diagnosis

2016 ◽  
Vol 9 (2) ◽  
pp. 57-65
Author(s):  
Justin Kurland ◽  
Peng Chen
Keyword(s):  
Graph Mining ◽  
Graph Database ◽  
Financial Organization ◽  
The Future ◽  
The Individual ◽  
Mining Algorithms ◽  
The Relationship ◽  
Authentic Activities

Formerly existing graph mining algorithms regularly accept that database is generally static. To defeat that we proposed another algorithm which manages extensive database including the components which catches the properties of the graph in a couple of parameters and check the relationship among them in both left and additionally right course, in this way embracing DFS and in addition BFS approach. It furthermore discovers the subgraph by traversing the graph and removing the planned routine. The proposed calculation is utilized for identification of wrongdoing as a part of BANK & Financial organization by catching the properties and distinguishing the relationship and affiliations that may exist between the individual required in that wrongdoing which keep a few violations that may happen in future. We have utilized the Neo-ECLIPSE for the execution of proposed calculation and Neo4j is the graph database utilized for evaluation. On the off chance that a man endeavoring to confer fraud or engage in some kind of illicit movement, they will endeavor to pass on their activities as near authentic activities as could reasonably be expected. Here in this paper, we are giving the data that a man who is in beginning the phase of the fraud, what co-related wrongdoings or illicit exercises he can do in future. The future exercises that can be performed by the individual can be ceased by demonstrating the associations with the entries saved in the database.

Mining Frequent Subgraph Patterns from Uncertain Graph Data

2010 ◽  
Vol 22 (9) ◽  
pp. 1203-1218 ◽  
Author(s):  
Zhaonian Zou ◽  
Jianzhong Li ◽  
Hong Gao ◽  
Shuo Zhang
Keyword(s):  
Uncertain Graph ◽  
Graph Data ◽  
Frequent Subgraph

scholarly journals A qualitative survey on frequent subgraph mining

2018 ◽  
Vol 8 (1) ◽  
pp. 194-209 ◽  
Author(s):  
Büsra Güvenoglu ◽  
Belgin Ergenç Bostanoglu
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Research Area ◽  
Heterogeneous Data ◽  
Graph Representation ◽  
Frequent Subgraph Mining ◽  
Subgraph Mining ◽  
Frequent Subgraph ◽  
Input Type ◽  
Frequent Subgraphs

AbstractData mining is a popular research area that has been studied by many researchers and focuses on finding unforeseen and important information in large databases. One of the popular data structures used to represent large heterogeneous data in the field of data mining is graphs. So, graph mining is one of the most popular subdivisions of data mining. Subgraphs that are more frequently encountered than the user-defined threshold in a database are called frequent subgraphs. Frequent subgraphs in a database can give important information about this database. Using this information, data can be classified, clustered and indexed. The purpose of this survey is to examine frequent subgraph mining algorithms (i) in terms of frequent subgraph discovery process phases such as candidate generation and frequency calculation, (ii) categorize the algorithms according to their general attributes such as input type, dynamicity of graphs, result type, algorithmic approach they are based on, algorithmic design and graph representation as well as (iii) to discuss the performance of algorithms in comparison to each other and the challenges faced by the algorithms recently.

scholarly journals Map Reduce Based Optimized Frequent Subgraph Mining Algorithm for Large Graph Database

2020 ◽  
Vol 9 (3) ◽  
pp. 3131-3139
Keyword(s):  
Distributed System ◽  
Real World ◽  
Transportation Network ◽  
Vital Role ◽  
Graph Database ◽  
Frequent Subgraph Mining ◽  
Large Graph ◽  
Subgraph Mining ◽  
Frequent Subgraph ◽  
Centralized System

Distributed System, plays a vital role in Frequent Subgraph Mining (FSM) to extract frequent subgraph from Large Graph database. It help to reduce in memory requirements, computational costs as well as increase in data security by distributing resources across distributed sites, which may be homogeneous or heterogeneous. In this paper, we focus on the problem related complexity of data arises in centralized system by using MapReduce framework. We proposed a MapReduced based Optimized Frequent Subgrph Mining (MOFSM) algorithm in MapReduced framework for large graph database. We also compare our algorithm with existing methods using four real-world standard datasets to verify that better solution with respect to performance and scalability of algorithm. These algorithms are used to extract subgraphs in distributed system which is important in real-world applications, such as computer vision, social network analysis, bio-informatics, financial and transportation network.

scholarly journals On-the-Fly Output Compression for Join-Based Graph Mining Algorithms

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 64008-64022 ◽  
Author(s):  
Mostofa Kamal Rasel ◽  
Young-Koo Lee
Keyword(s):  
Graph Mining ◽  
Mining Algorithms
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