Graph Embedding Using Constant Shift Embedding

• Published in: Recognizing Patterns in Signals, Speech, Images and Videos pp. 83 - 92
• Main Authors: Jouili, Salim, Tabbone, Salvatore
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2010
• Series: Lecture Notes in Computer Science
• Subjects: graph classification; graph embedding; Structural pattern recognition
• ISBN: 9783642177101; 3642177107
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-17711-8_9

In the literature, although structural representations (e.g. graph) are more powerful than feature vectors in terms of representational abilities, many robust and efficient methods for classification (unsupervised and supervised) have been developed for feature vector representations. In this paper, we propose a graph embedding technique based on the constant shift embedding which transforms a graph to a real vector. This technique gives the abilities to perform the graph classification tasks by procedures based on feature vectors. Through a set of experiments we show that the proposed technique outperforms the classification in the original graph domain and the other graph embedding techniques.