Graph Mining : An Overview
Christian Borgelt2009
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Abstract
In the early years of data mining and knowledge discovery in databases, method development focused on rigidly and plainly structured data. Most often efforts were even confined to data that can be represented as a simple table, which describes a set of sample cases by attribute-value pairs. Recent years, however, have seen a constantly growing interest in the analysis of more complex data, with a less rigid and/or more sophisticated structure.
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ACM SIGKDD Explorations Newsletter, 2003
The need for mining structured data has increased in the past few years. One of the best studied data structures in computer science and discrete mathematics are graphs. It can therefore be no surprise that graph based data mining has become quite popular in the last few years.This article introduces the theoretical basis of graph based data mining and surveys the state of the art of graph-based data mining. Brief descriptions of some representative approaches are provided as well.
Graph-based data mining
IEEE Intelligent Systems, 2000
at Arlington THE LARGE AMOUNT OF DATA collected today is quickly overwhelming researchers' abilities to interpret the data and discover interesting patterns in it. In response to this problem, researchers have developed techniques and systems for discovering concepts in databases. 1-3 Much of the collected data, however, has an explicit or implicit structural component (spatial or temporal), which few discovery systems are designed to handle. 4 So, in addition to the need to accelerate data mining of large databases, there is an urgent need to develop scalable tools for discovering concepts in structural databases. One method for discovering knowledge in structural data is the identification of common substructures within the data. Substructure discovery is the process of identifying concepts describing interesting and repetitive substructures within structural data. The discovered substructure concepts allow abstraction from the detailed data structure and provide relevant attributes for interpreting the data. The substructure discovery method is the basis of Subdue, which performs data mining on databases represented as graphs. The system performs two key data-mining techniques: unsupervised pattern discovery and supervised concept learning from examples. Our test applications have demonstrated the scalability and effectiveness of these techniques on a variety of structural databases.
A Brief Review on Application of Graph Theory in Data Mining
2016
Graph theory is becoming progressively important as it is applied to other fields of mathematics, science and technology. It is being actively used in areas as varied as biochemistry, electrical engineering, computer science and operations research. The main application of graph theory in data mining is graph mining. The need for mining structured data has increased in the past few years. Graphs are one of the best studied data structures in computer science and discrete mathematics. The relational aspect of data is explained by graph mining. The main aim of graph mining is to provide new principles and effective algorithms to mine topological substructures embedded in graph data. This article provides a brief review on four theoretical based approaches of graph based data mining. Brief description of application of graph mining is also provided.
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Performance Evaluation of Frequent Subgraph Discovery Techniques
Mathematical Problems in Engineering, 2014
Due to rapid development of the Internet technology and new scientific advances, the number of applications that model the data as graphs increases, because graphs have highly expressive power to model a complicated structure. Graph mining is a wellexplored area of research which is gaining popularity in the data mining community. A graph is a general model to represent data and has been used in many domains such as cheminformatics, web information management system, computer network, and bioinformatics, to name a few. In graph mining the frequent subgraph discovery is a challenging task. Frequent subgraph mining is concerned with discovery of those subgraphs from graph dataset which have frequent or multiple instances within the given graph dataset. In the literature a large number of frequent subgraph mining algorithms have been proposed; these included FSG, AGM, gSpan, CloseGraph, SPIN, Gaston, and Mofa. The objective of this research work is to perform quantitative comparison of the above listed techniques. The performances of these techniques have been evaluated through a number of experiments based on three different state-of-the-art graph datasets. This novel work will provide base for anyone who is working to design a new frequent subgraph discovery technique.
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Proc. Workshop on Data Mining for …, 2007
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Graphsig: A scalable approach to mining significant subgraphs in large graph databases
Data Engineering, 2009. ICDE'09. IEEE …, 2009
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Pre-Requisites in Graph Based Approach
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Association rule mining is a function of data mining research domain and frequent pattern mining is an essential part of it. Most of the previous studies on mining frequent patterns based on an Apriori approach, which required more number of database scans and operations for counting pattern supports in the database. Since the size of each set of transaction may be massive that it makes difficult to perform traditional data mining tasks. This research intends to propose a graph structure that captures only those itemsets that needs to define a sufficiently immense dataset into a submatrix representing important weights and does not give any chance to outliers. We have devised a strategy that covers significant facts of data by drilling down the large data into a succinct form of an Adjacency Matrix at different stages of mining process. The graph structure is so designed that it can be easily maintained and the trade off in compressing the large data values is reduced. Experimental ...
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Seventh International Conference on Digital Information Management (ICDIM 2012), 2012
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Managing and Mining Graph Data
Advances in Database Systems, 2010
Graph mining and management has become an important topic of research recently because of numerous applications to a wide variety of data mining problems in computational biology, chemical data analysis, drug discovery and communication networking. Traditional data mining and management algorithms such as clustering, classification, frequent pattern mining and indexing have now been extended to the graph scenario. This book contains a number of chapters which are carefully chosen in order to discuss the broad research issues in graph management and mining. In addition, a number of important applications of graph mining are also covered in the book. The purpose of this chapter is to provide an overview of the different kinds of graph processing and mining techniques, and the coverage of these topics in this book.
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Survey on Graph Pattern Mining Approach
INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH (IJEDR) (ISSN:2321-9939), 2014
Aim of Data Mining is to extract significant and Useful knowledge from the Data. Data Stored in the database may be any type such as text, images and videos so on. Due to increasing the size of the data and storing such data is becoming complex. Data mining algorithms are facing the challenges for storing such data. Graph become more important in storing and visualizing this complicated data (i.e. chemical datasets, biological dataset, XML datasets, Social networks datasets, the web datasets etc.) In this paper it is discussed about different Algorithm used for Graph Mining and different techniques used for Graph Mining.
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A Survey of Frequent Subgraph Mining Algorithms
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Abstract Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at:(i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective.
Analysis of data in the form of graphs
We discuss the problem of extending data mining approaches to cases in which data points arise in the form of individual graphs. Being able to find the intrinsic low-dimensionality in ensembles of graphs can be useful in a variety of modeling contexts, especially when coarse-graining the detailed graph information is of interest. One of the main challenges in mining graph data is the definition of a suitable pairwise similarity metric in the space of graphs. We explore two practical solutions to solving this problem: one based on finding subgraph densities, and one using spectral information. The approach is illustrated on three test data sets (ensembles of graphs); two of these are obtained from standard graph generating algorithms, while the graphs in the third example are sampled as dynamic snapshots from an evolving network simulation.
Clustering Improves the Exploration of Graph Mining Results
IFIP The International Federation for Information Processing, 2007
A b s t r a c t . Mining frequent subgraphs is an area of research where we have a given set of graphs, and where we seeirch for (connected) subgraphs contained in many of these graphs. Each graph can be seen as a transaction, or as a molecule -as the techniques applied in this paper are used in (bio)chemical analysis. In this work we will discuss an application that enables the user to further explore the results from a frequent subgraph mining algorithm. Such an algorithm gives the frequent subgraphs, also referred to as fragments, in the graphs in the dataset. Next to frequent subgraphs the algorithm also provides a lattice that models sub-and supergraph relations among the fragments, which can be explored with our application. The lattice can also be used to group fragments by means of clustering algorithms, and the user can easily browse from group to group. The application can also display only a selection of groups that occur in almost the same set of molecules, or on the contrary in different molecules. This allows one to see which patterns cover different or similar parts of the dataset.
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