On the Correlation of Graph Edit Distance and L 1 Distance in the Attribute Statistics Embedding Space
نویسندگان
چکیده
Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected.
منابع مشابه
The Existence Theorem for Contractive Mappings on $wt$-distance in $b$-metric Spaces Endowed with a Graph and its Application
In this paper, we study the existence and uniqueness of fixed points for mappings with respect to a $wt$-distance in $b$-metric spaces endowed with a graph. Our results are significant, since we replace the condition of continuity of mapping with the condition of orbitally $G$-continuity of mapping and we consider $b$-metric spaces with graph instead of $b$-metric spaces, under which can be gen...
متن کاملFixed point results in cone metric spaces endowed with a graph
In this paper, we prove the existence of fixed point for Chatterjea type mappings under $c$-distance in cone metric spaces endowed with a graph. The main results extend, generalized and unified some fixed point theorems on $c$-distance in metric and cone metric spaces.
متن کاملSpeeding Up Graph Edit Distance Computation with a Bipartite Heuristic
Graph edit distance is a dissimilarity measure for arbitrarily structured and arbitrarily labeled graphs. In contrast with other approaches, it does not suffer from any restrictions and can be applied to any type of graph, including hypergraphs [1]. Graph edit distance can be used to address various graph classification problems with different methods, for instance, k-nearest-neighbor classifie...
متن کاملMatching and Embedding through Edit-Union of Trees
This paper investigates a technique to extend the tree edit distance framework to allow the simultaneous matching of multiple tree structures. This approach extends a previous result that showed the edit distance between two trees is completely determined by the maximum tree obtained from both tree with node removal operations only. In our approach we seek the minimum structure from which we ca...
متن کاملGeneric object recognition using graph embedding into a vector space
This paper describes a method for generic object recognition using graph structural expression. In recent years, generic object recognition by computer is finding extensive use in a variety of fields, including robotic vision and image retrieval. Conventional methods use a bag-of-features (BoF) approach, which expresses the image as an appearance frequency histogram of visual words by quantizin...
متن کامل