This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.Contents:Introduction and Basic ConceptsGraph MatchingGraph Edit DistanceGraph DataKernel MethodsGraph Embedding Using DissimilaritiesClassification Experiments with Vector Space Embedded GraphsClustering Experiments with Vector Space Embedded GraphsReadership: Professionals, academics, researchers and students in pattern recognition, machine perception/computer vision and artificial intelligence.