Graph drawing by force-directed placement
Software—Practice & Experience
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Mean and maximum common subgraph of two graphs
Pattern Recognition Letters
A Linear Programming Approach for the Weighted Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Search for Structural Matching
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Improved Simulated Annealing, Boltzmann Machine, and Attributed Graph Matching
Proceedings of the EURASIP Workshop 1990 on Neural Networks
GD '02 Revised Papers from the 10th International Symposium on Graph Drawing
Density Functions for Visual Attributes and Effective Partitioning in Graph Visualization
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units
IEEE Transactions on Visualization and Computer Graphics
EdgeLens: an interactive method for managing edge congestion in graphs
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Genetic-based search for error-correcting graph isomorphism
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Inexact graph matching for structural pattern recognition
Pattern Recognition Letters
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A method is proposed for quantifying differences between multichannel EEG coherence networks represented by functional unit (FU) maps. The approach is based on inexact graph matching for attributed relational graphs and graph averaging, adapted to FU maps. The mean of a set of input FU maps is defined in such a way that it not only represents the mean group coherence during a certain task or condition but also to some extent displays individual variations in brain activity. The definition of a mean FU map relies on a graph dissimilarity measure which takes into account both node positions and node or edge attributes. A visualization of the mean FU map is used with a visual representation of the frequency of occurrence of nodes and edges in the input FUs. This makes it possible to investigate which brain regions are more commonly involved in a certain task, by analysing the occurrence of an FU of the mean graph in the input FUs. Furthermore, our method gives the possibility to quantitatively compare individual FU maps by computing their distance to the mean FU map.