GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by up to >2x), maximal clique listing (by up to >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x), also obtaining better theoretical performance bounds

Consumer Behavior Analysis by Graph Mining Technique (post print version)

In this paper, we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential purchase pattern is reviewed. Then we propose to represent the complicated customer purchase behavior by a directed graph retaining temporal information in a purchase sequence and apply a graph mining technique to analyze the frequent occurring patterns. In this paper, we demonstrate through the case of healthy cooking oil analysis how graph mining technology helps us understand complex purchase behavior

Social Mining to Progress the Computational Efficiency using Mapreduce

Graphs are widely used in large scale social network analysis. Graph mining increasingly important in modelling complicated structures such as circuits, images, web, biological networks and social networks. The major problems occur in this graph mining are computational efficiency (CE) and frequent subgraph mining (FSM). Computational Efficiency describes the extent to which the time, effort or efficiency which use computing technology in information processing. Frequent Sub graph Mining is the mechanism of candidate generation without duplicates. FSM faces the problem on counting the instances of the patterns in the dataset and counting of instances for graphs. The main objective of this project is to address CE and FSM problems. The paper cited in the reference proposes an algorithm called Mirage algorithm to solve queries using subgraph mining. The proposed work focuses on enhancing An Iterative Map Reduce based Frequent Subgraph Mining Algorithm (MIRAGE) to consider optimum computational efficiency. The test data to be considered for this mining algorithm can be from any domains such as medical, text and social data2019;s (twitter). The major contributions are: an iterative MapReduce based frequent sub graph mining algorithm called MIRAGE used to address the frequent sub graph mining problem