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A Graduated Assignment Algorithm for Graph Matching
, 1996
"... A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, twoway (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational comp ..."
Abstract

Cited by 374 (15 self)
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complexity [O(lm), where l and m are the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching
Subgraph matching kernels for attributed graphs
 In ICML
, 2012
"... We propose graph kernels based on subgraph matchings, i.e. structurepreserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al., 2009) in general can not be applied to attributed graphs, our approach allows to rate map ..."
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Cited by 6 (0 self)
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We propose graph kernels based on subgraph matchings, i.e. structurepreserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al., 2009) in general can not be applied to attributed graphs, our approach allows to rate
Combining exhaustive and approximate methods for improved subgraph matching
 in Proc. Int. Workshop on Advances in Pattern Recognition
, 2007
"... Summary. Reams of different methods have been applied on the inexact graph matching problem in the last decades. In fact, there are two disjoint groups of approaches, exhaustive search and approximate methods. The first ones guarantee that the best solution is always found while the last ones genera ..."
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Cited by 1 (1 self)
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Summary. Reams of different methods have been applied on the inexact graph matching problem in the last decades. In fact, there are two disjoint groups of approaches, exhaustive search and approximate methods. The first ones guarantee that the best solution is always found while the last ones
MAGE: Matching Approximate Patterns in RichlyAttributed Graphs
"... Abstract—Given a large graph with millions of nodes and edges, say a social network where both its nodes and edges have multiple attributes (e.g., job titles, tie strengths), how to quickly find subgraphs of interest (e.g., a ring of businessmen with strong ties)? We present MAGE, a scalable, multic ..."
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Abstract—Given a large graph with millions of nodes and edges, say a social network where both its nodes and edges have multiple attributes (e.g., job titles, tie strengths), how to quickly find subgraphs of interest (e.g., a ring of businessmen with strong ties)? We present MAGE, a scalable
Sapper: Subgraph indexing and approximate matching in large graphs
 PVLDB
"... ABSTRACT With the emergence of new applications, e.g., computational biology, new software engineering techniques, social networks, etc., more data is in the form of graphs. Locating occurrences of a query graph in a large database graph is an important research topic. Due to the existence of noise ..."
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Cited by 23 (0 self)
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of noise (e.g., missing edges) in the large database graph, we investigate the problem of approximate subgraph indexing, i.e., finding the occurrences of a query graph in a large database graph with (possible) missing edges. The SAPPER method is proposed to solve this problem. Utilizing the hybrid
Approximating MinimumSize kConnected Spanning Subgraphs via Matching
 SIAM J. Comput
, 1998
"... Abstract: An efficient heuristic is presented for the problem of finding a minimumsize k connected spanning subgraph of an (undirected or directed) simple graph G =(V#E). There are four versions of the problem, and the approximation guarantees are as follows: minimumsize knode connected spann ..."
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Cited by 43 (3 self)
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Abstract: An efficient heuristic is presented for the problem of finding a minimumsize k connected spanning subgraph of an (undirected or directed) simple graph G =(V#E). There are four versions of the problem, and the approximation guarantees are as follows: minimumsize knode connected
Genetic Approximate Matching of Attributed Relational Graphs
"... Image segmentation algorithms identify meaningful spatial entities for contentbased image retrieval. One or several visual features are extracted for each entity. Based on the feature vectors of the spatial entities ..."
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Image segmentation algorithms identify meaningful spatial entities for contentbased image retrieval. One or several visual features are extracted for each entity. Based on the feature vectors of the spatial entities
Approximation of DensekSubgraph
, 2000
"... We consider the DENSEkSUBGRAPH problem, i.e., determine a block of k nodes of a weighted graph (of n nodes) such that the total edge weight within the subgraph induced by the block is maximized. we present two approximation algorithms for this problem which are based on linear programming (LP) and ..."
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Cited by 1 (0 self)
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We consider the DENSEkSUBGRAPH problem, i.e., determine a block of k nodes of a weighted graph (of n nodes) such that the total edge weight within the subgraph induced by the block is maximized. we present two approximation algorithms for this problem which are based on linear programming (LP
Decomposition Techniques for Subgraph Matching
"... Abstract. In the constraint programming framework, stateoftheart static and dynamic decomposition techniques are hard to apply to problems with complete initial constraint graphs. For such problems, we propose a hybrid approach of these techniques in the presence of global constraints. In particu ..."
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. In particular, we solve the subgraph isomorphism problem. Further we design specific heuristics for this hard problem, exploiting its special structure to achieve decomposition. The underlying idea is to precompute a static heuristic on a subset of its constraint network, to follow this static ordering until a
Results 1  10
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572,458