Discovering Fuzzy Structural Patterns for Graph Analytics

Many real-world datasets can be represented as attributed graphs that contain vertices, each of which is associated with a set of attribute values. Discovering clusters, or communities, which are structural patterns in these graphs, are one of the most important tasks in graph analysis. To perform t...

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Vydané v:IEEE transactions on fuzzy systems Ročník 26; číslo 5; s. 2785 - 2796
Hlavní autori: He, Tiantian, Chan, Keith C. C.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithm design and analysis
Algorithms
Analytics
Attributed graph
biological network
Clustering algorithms
Clusters
community detection
complex network
Datasets
fuzzy clustering
fuzzy graph clustering
fuzzy structural pattern
graph analytics
Graphical representations
Graphs
Image edge detection
Optimization
relational fuzzy c-means (FCM) clustering
social network
Social network services
Topology
ISSN:1063-6706, 1941-0034
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Abstract Many real-world datasets can be represented as attributed graphs that contain vertices, each of which is associated with a set of attribute values. Discovering clusters, or communities, which are structural patterns in these graphs, are one of the most important tasks in graph analysis. To perform the task, a number of algorithms have been proposed. Some of them detect clusters of particular topological properties, whereas some others discover them mainly based on attribute information. Also, most of the algorithms discover disjoint clusters only. As a result, they may not be able to detect more meaningful clusters hidden in the attributed graph. To do so more effectively, we propose an algorithm, called FSPGA, to discover fuzzy structural patterns for graph analytics. FSPGA performs the task of cluster discovery as a fuzzy-constrained optimization problem, which takes into consideration both the graph topology and attribute values. FSPGA has been tested with both synthetic and real-world graph datasets and is found to be efficient and effective at detecting clusters in attributed graphs. FSPGA is a promising fuzzy algorithm for structural pattern detection in attributed graphs.
AbstractList Many real-world datasets can be represented as attributed graphs that contain vertices, each of which is associated with a set of attribute values. Discovering clusters, or communities, which are structural patterns in these graphs, are one of the most important tasks in graph analysis. To perform the task, a number of algorithms have been proposed. Some of them detect clusters of particular topological properties, whereas some others discover them mainly based on attribute information. Also, most of the algorithms discover disjoint clusters only. As a result, they may not be able to detect more meaningful clusters hidden in the attributed graph. To do so more effectively, we propose an algorithm, called FSPGA, to discover fuzzy structural patterns for graph analytics. FSPGA performs the task of cluster discovery as a fuzzy-constrained optimization problem, which takes into consideration both the graph topology and attribute values. FSPGA has been tested with both synthetic and real-world graph datasets and is found to be efficient and effective at detecting clusters in attributed graphs. FSPGA is a promising fuzzy algorithm for structural pattern detection in attributed graphs.
Author Chan, Keith C. C.
He, Tiantian
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SubjectTerms Algorithm design and analysis
Algorithms
Analytics
Attributed graph
biological network
Clustering algorithms
Clusters
community detection
complex network
Datasets
fuzzy clustering
fuzzy graph clustering
fuzzy structural pattern
graph analytics
Graphical representations
Graphs
Image edge detection
Optimization
relational fuzzy c-means (FCM) clustering
social network
Social network services
Topology
Title Discovering Fuzzy Structural Patterns for Graph Analytics
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