Organizing Large Structural Modelbases
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Content-Based Image Retrieval at the End of the Early Years
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On Median Graphs: Properties, Algorithms, and Applications
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Graph Edit Distance from Spectral Seriation
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Graph clustering using the weighted minimum common supergraph
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Graph embedding in vector spaces by means of prototype selection
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Comparing graph similarity measures for graphical recognition
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Graph embedding using constant shift embedding
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A generic framework for median graph computation based on a recursive embedding approach
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Towards performance evaluation of graph-based representation
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Organization of Relational Models for Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph database retrieval based on metric-trees
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Component retrieval based on a database of graphs for Hand-Written Electronic-Scheme Digitalisation
Expert Systems with Applications: An International Journal
| Hi-index | 0.01 |
In this paper, we introduce a novel method for graph indexing. We propose a hypergraph-based model for graph data sets by allowing cluster overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we index the graph set by the hyperedge centroids. This model is interesting to traverse the data set and efficient to retrieve graphs.