In recent years graph embedding has emerged as a promising solution for enabling the expressive, convenient, powerful but computational expensive graphbasedrepresentations to benefit from mature, less expensive and ...
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graph centrality has been extensively applied in Social Network Analysis to model the interaction of actors and the information flow inside a graph. In this paper, we investigate the usage of graph centralities in the...
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We propose a new fast algorithm for solving the Maximum Common Subgraph (MCS) problem. MCS is an NP-complete problem. In this paper, we focus on a special class of graphs, i.e. Planar Triangulation graphs, which are c...
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the recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a...
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the proceedings contain 34 papers. the topics discussed include: a global method for reducing multidimensional size graphs;graph descriptors from B-matrix representation;dimensionality reduction for graph of words emb...
ISBN:
(纸本)9783642208430
the proceedings contain 34 papers. the topics discussed include: a global method for reducing multidimensional size graphs;graph descriptors from B-matrix representation;dimensionality reduction for graph of words embedding;entropy versus heterogeneity for graphs;learning generative graph prototypes using simplified von Neumann entropy;information-geometric graph indexing from bags of partial node coverages;maximum likelihood for Gaussians on graphs;towards performance evaluation of graph-based representation;measuring the distance of generalized maps;aggregated search in graph databases: preliminary results;speeding up graph edit distance computation through fast bipartite matching;two new graph kernels and applications to chemoinformatics;parallel graduated assignment algorithm for multiple graph matching based on a common labeling;and smooth simultaneous structural graph matching and point-set registration.
In this paper, we investigate the Max-Cut problem and propose a probabilistic heuristic to address its classic and weighted version. Our approach is based on the Estimation of Distribution Algorithm (EDA) that creates...
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the proceedings contain 34 papers. the special focus in this conference is on graph-basedrepresentations in patternrecognition. the topics include: Speeding Up graph Edit Distance Computation through Fast Bipartite ...
ISBN:
(纸本)9783642208430
the proceedings contain 34 papers. the special focus in this conference is on graph-basedrepresentations in patternrecognition. the topics include: Speeding Up graph Edit Distance Computation through Fast Bipartite Matching;two New graph Kernels and Applications to Chemoinformatics;generalized Learning graph Quantization;parallel Graduated Assignment Algorithm for Multiple graph Matching based on a Common Labelling;smooth Simultaneous Structural graph Matching and Point-Set Registration;automatic Learning of Edit Costs based on Interactive and Adaptive graphrecognition;exploration of the Labelling Space Given graph Edit Distance Costs;graph Matching based on Dot Product Representation of graphs;indexing with Well-Founded Total Order for Faster Subgraph Isomorphism Detection;graph Descriptors from B-Matrix Representation;graph Transduction as a Non-cooperative Game;a graph-based Approach to Feature Selection;spatio-Temporal Extraction of Articulated Models in a graph Pyramid;semi-supervised Segmentation of 3D Surfaces Using a Weighted graph Representation;convexity Grouping of Salient Contours;hierarchical Interactive Image Segmentation Using Irregular Pyramids;tiled Top-Down Pyramids and Segmentation of Large Histological Images;segmentation of Similar Images Using graph Matching and Community Detection;automatic Street graph Construction in Sketch Maps;people Re-identification by graph Kernels Methods;dimensionality Reduction for graph of Words Embedding;automatic Labeling of Handwritten Mathematical Symbols via Expression Matching;structure-based Evaluation Methodology for Curvilinear Structure Detection Algorithms;keygraphs for Sign Detection in Indoor Environments by Mobile Phones;classification of graph Sequences Utilizing the Eigenvalues of the Distance Matrices and Hidden Markov Models;using Kernels on Hierarchical graphs in Automatic Classification of Designs;entropy versus Heterogeneity for graphs.
this paper is concerned with a problem of detecting relational changes. Many kinds of graph data including social networks are increasing nowadays. In such a graph, the relationships among vertices are changing day by...
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ISBN:
(纸本)9783642449499;9783642449482
this paper is concerned with a problem of detecting relational changes. Many kinds of graph data including social networks are increasing nowadays. In such a graph, the relationships among vertices are changing day by day. therefore, it would be worth investigating a data mining method for detecting significant patterns informing us about what changes. We present in this paper a general framework for detecting relational changes over two graphs to be contrasted. Our target pattern with relational change is defined as a set of vertices common in bothgraphs in which the vertices are almost disconnected in one graph, while densely connected in the other. We formalize such a target patternbased on the notions of modularity and k-plex. A depth-first algorithm for the mining task is designed as an extension of k-plex enumerators with some pruning mechanisms. Our experimental results show usefulness of the proposed method for two pairs of graphs representing actual reply-communications among Twitter users and word co-occurrence relations in Japanese news articles.
Multi-labelled graphs are a powerful and versatile tool for modelling real applications in diverse domains such as communication networks, social networks, and autonomic systems, among others. Due to dynamic nature of...
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Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In patternrecognition, feature selection improves the performance of classification by reducing the feature sp...
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