Reservoir-based sampling over large graph streams to estimate triangle counts and node degrees

2020 ◽  
Vol 108 ◽  
pp. 244-255
Author(s):  
Lingling Zhang ◽  
Hong Jiang ◽  
Fang Wang ◽  
Dan Feng ◽  
Yanwen Xie
Keyword(s):  
Large Graph ◽  
Graph Streams

Approximately counting triangles in large graph streams including edge duplicates with a fixed memory usage

2017 ◽  
Vol 11 (2) ◽  
pp. 162-175 ◽  
Author(s):  
Pinghui Wang ◽  
Yiyan Qi ◽  
Yu Sun ◽  
Xiangliang Zhang ◽  
Jing Tao ◽  
...  
Keyword(s):  
Memory Usage ◽  
Large Graph ◽  
Graph Streams

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.

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