Efficient subgraph matching using topological node feature constraints

Dahm, Nicholas; Bunke, Horst; Caelli, Terry; Gao, Yongsheng

doi:10.1016/j.patcog.2014.05.018

Cited by 20 publications

(15 citation statements)

References 30 publications

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“…All the techniques that are presented above from the literature show that the local information plays a vital role in enhancing the affinity matrix to improve clustering results. To capture the local properties effectively, the Topological Node Features (TNFs) [7] have been utilized from the graph representation of the data. A TNF is mainly defined as topological information when viewed from any particular node of a graph.…”

Section: E Topological Node Features Based Spectral Clustering (Sc-tnf)mentioning

confidence: 99%

“…TNFs have been used in Sorlin and Solnon [23] to solve the subgraph isomorphism problem, as they capture the local structure in the data effectively. Dahm et al [7] used novel TNFs for speeding up subgraph isomorphism detection. The work of Dahm et al [7] has been used for building the affinity measure that effectively captures local information.…”

Section: E Topological Node Features Based Spectral Clustering (Sc-tnf)mentioning

confidence: 99%

“…Dahm et al [7] used novel TNFs for speeding up subgraph isomorphism detection. The work of Dahm et al [7] has been used for building the affinity measure that effectively captures local information. The main TNFs used in the proposed affinity measure are degree, Clustering Coefficient (CC), and Summation Index (SI).…”

Section: E Topological Node Features Based Spectral Clustering (Sc-tnf)mentioning

confidence: 99%

“…Dahm et al [7] in their work define recursively as a sum of node degrees of neighboring nodes as: quantifies how much structurally differs from . In addition to these TNFs, the proposed affinities utilize another feature ( ), the number of common points in the neighborhoods of , , that is = | ( ) ∩ ( )| .…”

Section: Tnf Based Frameworkmentioning

confidence: 99%

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Enhanced Affinity for Spectral Clustering using Topological Node Features (TNFS)

Chintalapati,

Rachakonda

2019

IJEAT

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Data clustering is an active topic of research as it has applications in various fields such as biology, management, statistics, pattern recognition, etc. Spectral Clustering (SC) has gained popularity in recent times due to its ability to handle complex data and ease of implementation. A crucial step in spectral clustering is the construction of the affinity matrix, which is based on a pairwise similarity measure. The varied characteristics of datasets affect the performance of a spectral clustering technique. In this paper, we have proposed an affinity measure based on Topological Node Features (TNFs) viz., Clustering Coefficient (CC) and Summation index (SI) to define the notion of density and local structure. It has been shown that these features improve the performance of SC in clustering the data. The experiments were conducted on synthetic datasets, UCI datasets, and the MNIST handwritten datasets. The results show that the proposed affinity metric outperforms several recent spectral clustering methods in terms of accuracy.

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Section: E Topological Node Features Based Spectral Clustering (Sc-tnf)mentioning

confidence: 99%

Section: E Topological Node Features Based Spectral Clustering (Sc-tnf)mentioning

confidence: 99%

Section: E Topological Node Features Based Spectral Clustering (Sc-tnf)mentioning

confidence: 99%

Section: Tnf Based Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Affinity for Spectral Clustering using Topological Node Features (TNFS)

Chintalapati,

Rachakonda

2019

IJEAT

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“…These networks raise interesting questions regarding their structure. One of those questions asks whether a given network contains a particular pattern, which typically represents a specific behaviour of interest [1,7,15]. The problem of locating a particular pattern in the given network can be restated as a problem of locating a subgraph isomorphic to the given pattern graph in the network graph.…”

Section: Introductionmentioning

confidence: 99%

Efficient Implementation of Color Coding Algorithm for Subgraph Isomorphism Problem

Malík

Suchý

Valla

2019

Lecture Notes in Computer Science

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We consider the subgraph isomorphism problem where, given two graphs G (source graph) and F (pattern graph), one is to decide whether there is a (not necessarily induced) subgraph of G isomorphic to F . While many practical heuristic algorithms have been developed for the problem, as pointed out by McCreesh et al. [JAIR 2018], for each of them there are rather small instances which they cannot cope. Therefore, developing an alternative approach that could possibly cope with these hard instances would be of interest.A seminal paper by Alon, Yuster and Zwick [J. ACM 1995] introduced the color coding approach to solve the problem, where the main part is a dynamic programming over color subsets and partial mappings. As with many exponential-time dynamic programming algorithms, the memory requirements constitute the main limiting factor for its usage. Because these requirements grow exponentially with the treewidth of the pattern graph, all existing implementations based on the color coding principle restrict themselves to specific pattern graphs, e.g., paths or trees. In contrast, we provide an efficient implementation of the algorithm significantly reducing its memory requirements so that it can be used for pattern graphs of larger treewidth. Moreover, our implementation not only decides the existence of an isomorphic subgraph, but it also enumerates all such subgraphs (or given number of them).We provide an extensive experimental comparison of our implementation to other available solvers for the problem.

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