Exact and Inexact Graph Matching: Methodology and Applications

  title={Exact and Inexact Graph Matching: Methodology and Applications},
  author={Kaspar Riesen and Xiaoyi Jiang and Horst Bunke},
  booktitle={Managing and Mining Graph Data},
Graphs provide us with a powerful and flexible representation formalism which can be employed in various fields of intelligent information processing. The process of evaluating the similarity of graphs is referred to as graph matching. Two approaches to this task exist, viz. exact and inexact graph matching. The former approach aims at finding a strict correspondence between two graphs to be matched, while the latter is able to cope with errors and measures the difference of two graphs in a… 
A novel graph matching method based on the local and global distance information of the graph nodes
This paper proposes an approximate graph matching method that first constructs an association graph with nodes representing the candidate correspondences between the two original graphs, then constructs an affinity matrix based on the local and global distance information between the original graphs’ nodes.
A Novel Method for Graph Matching
This work proposes an approximate graph matching method that obtains the matching between the nodes of the two original graphs by discretizing the distribution on the basis of the Hungarian algorithm.
Graph matching based on local and global information of the graph nodes
This method formulates the problem of computing the correspondences between two graphs as a problem of selecting nodes on an association graph and applies the Hungarian algorithm to obtains an approximate matching between the original two graphs.
KSGM: Keynode-driven Scalable Graph Matching
Experiments show that the proposed keynode-driven scalable graph matching algorithms produce alignments that are as accurate as (or better than) the state-of-the-art algorithms, with significantly faster online executions.
High efficiency and quality: large graphs matching
This paper proposes a novel two-step approach that can efficiently match two large graphs over thousands of nodes with high matching quality and gives the optimality of the refinement and discusses how to randomly refine the matching with different combinations.
Efficient geometric graph matching using vertex embedding
This paper introduces an iterative matching algorithm that matches two graphs using their similarity in the Euclidean space and shows that this approach outperforms existing graph matching approaches in terms of matching quality and runtime.
Mining interesting frequent patterns in a single graph using inexact matching
This work introduces the algorithm AGraP, which, by allowing patterns that can have structural differences, in vertices as well as in edges, respect to their occurrences, is able to find patterns missed by other state of the art algorithms, and introduces the algorithms CloseAFG, MaxAFG and IntAFG that, by focusing on closed, maximal or interesting patterns, respectively, are able to reduce the amount of mined patterns by AGraC.
An approach to merging of two community subgraphs to form a community graph using graph mining techniques
  • B. Rao, A. Mitra
  • Computer Science
    2014 IEEE International Conference on Computational Intelligence and Computing Research
  • 2014
New algorithms which merge two community subgraphs in an efficient and simpler way are proposed which explains about finding the order of merged communities and to make available of initial form of merged community matrix.
Querying and extracting heterogeneous graphs from structured data and unstrutured content
A new graph model called the SPIDER-Graph is defined, which models complex objects and permits to define heterogeneous graphs, and a set of algorithms to extract the content of a database from an enterprise and to represent it in this new model are developed.
Overview of Existing Software Tools for Graph Matching
The report aims to give an overview of publicly available software toolkits for graph matching and existing graph datasets for graphs matching benchmarking as well as common data structures to store graphs.


Reactive Tabu Search for Measuring Graph Similarity
This paper addresses the problem of computing this generic similarity measure and describes two algorithms: a greedy algorithm that quickly computes sub-optimal solutions, and a reactive Tabu search algorithm that may improve these solutions.
Generalized Graph Matching for Data Mining and Information Retrieval
The concept of subgraph isomorphism is substantially extended such that it can cope with don't care symbols, variables, and constraints and leads to a powerful graph matching methodology which can be used for advanced graph based data mining.
A (sub)graph isomorphism algorithm for matching large graphs
The algorithm is improved here to reduce its spatial complexity and to achieve a better performance on large graphs; its features are analyzed in detail with special reference to time and memory requirements.
Approximate graph edit distance computation by means of bipartite graph matching
TALE: A Tool for Approximate Large Graph Matching
A novel indexing method that incorporates graph structural information in a hybrid index structure that achieves high pruning power and the index size scales linearly with the database size is proposed.
Exact and approximate graph matching using random walks
A general framework for graph matching which is suitable for different problems of pattern recognition, and is very well-suited for dealing with partial and approximate graph matching problems, derived for instance from image retrieval tasks.
A graph distance metric based on the maximal common subgraph
A graph distance metric combining maximum common subgraph and minimum common supergraph
Thirty Years Of Graph Matching In Pattern Recognition
This paper will try to characterize the role that graphs play within the Pattern Recognition field, and presents two taxonomies that include almost all the graph matching algorithms proposed from the late seventies and describes the different classes of algorithms.
Matching graphs with unique node labels
An application of the considered class of graphs and related matching algorithms to the classification and detection of abnormal events in computer networks and the matching of large graphs, consisting of thousands of nodes, computationally tractable is discussed.