A new method of structural graph matching is introduced and compared against an existing method and against the maximum common subgraph. The method is approximate with polynomial bounds on both memory and on the worst-case compute effort. Methods work on arbitrary types of graphs and tests with strongly regular graphs are included. No node or edge colors are needed in the methods; the common subgraph is extracted based in structural comparisons only. Monte Carlo trials are benchmarked with 100% additional (clutter) nodes. Results are shown to be typically within 1-2 nodes of the maximum common subgraph. Over 7500 test trials are reported with graphs up to 100 nodes
Matching is a set of edges in a graph which each of the edge does not share a common vertex. Maximum...
Approximation of graph edit distance in polynomial time enables us to compare large, arbitrarily lab...
By advancing the idea of finding width in bipartite graphs and basic definitions in matching theory,...
A new method of structural graph matching is introduced and compared against an existing method and ...
The ‘basis graph’ approach to structural matching uses a fixed set of small (4 node) graphs to chara...
International audienceGraphs are an extremely general and powerful data structure. In pattern recogn...
Graph matching plays an essential role in many real applications. In this paper, we study how to mat...
This paper describes an efficient algorithm for inexact graph matching. The method is purely structu...
19Graphs provide an efficient tool for object representation in various computer vision applications...
Graph matching algorithms are gaining more and more interest in the last years from different scient...
Recent advances in computer vision, information retrieval, and molecular biology amply demonstrate t...
Graph pattern matching is typically defined in terms of sub-graph isomorphism, which makes it an np-...
The graph matching optimization problem is an essential component for many tasks in computer vision,...
A new graph similarity calculation procedure is introduced for comparing labeled graphs. Given a min...
Graph pattern matching is commonly used in a variety of emerging applications such as social network...
Matching is a set of edges in a graph which each of the edge does not share a common vertex. Maximum...
Approximation of graph edit distance in polynomial time enables us to compare large, arbitrarily lab...
By advancing the idea of finding width in bipartite graphs and basic definitions in matching theory,...
A new method of structural graph matching is introduced and compared against an existing method and ...
The ‘basis graph’ approach to structural matching uses a fixed set of small (4 node) graphs to chara...
International audienceGraphs are an extremely general and powerful data structure. In pattern recogn...
Graph matching plays an essential role in many real applications. In this paper, we study how to mat...
This paper describes an efficient algorithm for inexact graph matching. The method is purely structu...
19Graphs provide an efficient tool for object representation in various computer vision applications...
Graph matching algorithms are gaining more and more interest in the last years from different scient...
Recent advances in computer vision, information retrieval, and molecular biology amply demonstrate t...
Graph pattern matching is typically defined in terms of sub-graph isomorphism, which makes it an np-...
The graph matching optimization problem is an essential component for many tasks in computer vision,...
A new graph similarity calculation procedure is introduced for comparing labeled graphs. Given a min...
Graph pattern matching is commonly used in a variety of emerging applications such as social network...
Matching is a set of edges in a graph which each of the edge does not share a common vertex. Maximum...
Approximation of graph edit distance in polynomial time enables us to compare large, arbitrarily lab...
By advancing the idea of finding width in bipartite graphs and basic definitions in matching theory,...