Embedding of Graphs with discrete Attributes via Label frequencies

  title={Embedding of Graphs with discrete Attributes via Label frequencies},
  author={Jaume Gibert and Ernest Valveny and Horst Bunke},
  journal={Int. J. Pattern Recognit. Artif. Intell.},
Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies… 

Figures and Tables from this paper

Optimized dissimilarity space embedding for labeled graphs
Real-Valued Embeddings and Sketches for Fast Distance and Similarity Estimation
Methods and algorithms for fast estimation of data distance/similarity measures from formed real-valued vectors of small dimension using random projection and sampling are considered.
Improving bipartite graph matching by assessing the assignment confidence
Estimating Graph Edit Distance Using Lower and Upper Bounds of Bipartite Approximations
The concept of graph edit distance (GED) is still one of the most flexible and powerful graph matching approaches available. Yet, exact computation of GED can be solved in exponential time complexity
Inexact graph matching : application to 2D and 3D Pattern Recognition. (Appariement inexact de graphes : application à la reconnaissance de formes 2D et 3D)
A complete graph based framework for Kite recognition on satellite images is presented and a more general graph matching approach founded on a new formalization based on the stable marriage problem is proposed.
Graph Matching and Learning in Pattern Recognition in the Last 10 Years
In this paper, we examine the main advances registered in the last ten years in Pattern Recognition methodologies based on graph matching and related techniques, analyzing more than 180 papers; the...


Dimensionality Reduction for Graph of Words Embedding
Two well-known techniques for dimensionality reduction, kernel principal component analysis and independent component analysis, are applied to the embedded graphs and their performance compared to the classification of the original vectors is discussed.
Graph Embedding Using Constant Shift Embedding
This paper proposes a graph embedding technique based on the constant shift embedding which transforms a graph to a real vector and gives the abilities to perform the graph classification tasks by procedures based on feature vectors.
Graph Classification and Clustering Based on Vector Space Embedding
A fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs based on dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces is proposed.
Vocabulary Selection for Graph of Words Embedding
The Graph of Words Embedding is extended to graphs with n-dimensional continuous attributes by selecting node representatives by proposing three different discretization procedures for the attribute space and experimentally evaluating the dependence on both the selector and the number of node representatives.
Marginalized Kernels Between Labeled Graphs
A new kernel function between two labeled graphs that is based on an infinite dimensional feature space, so it is fundamentally different from other string or tree kernels based on dynamic programming and presents promising empirical results in classification of chemical compounds.
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
A repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications is introduced, to make the different approaches in graph based machine learning better comparable.
Pattern Vectors from Algebraic Graph Theory
The spectral decomposition of the Laplacian matrix is shown to be used to construct symmetric polynomials that are permutation invariants that can be used as graph features which can be encoded in a vectorial manner.
Spectral embedding of graphs
Self-organizing maps for learning the edit costs in graph matching
  • M. Neuhaus, H. Bunke
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2005
A system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels are proposed that adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity from different classes decreases.
Bridging the Gap between Graph Edit Distance and Kernel Machines
  • M. Neuhaus, H. Bunke
  • Computer Science
    Series in Machine Perception and Artificial Intelligence
  • 2007
The authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time.