Graph Embedding Based on Nodes Attributes Representatives and a Graph of Words Representation

  • Jaume Gibert
  • Ernest Valveny
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)


Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jaume Gibert
    • 1
  • Ernest Valveny
    • 1
  1. 1.Centre de Visió per ComputadorUniversitat Autònoma de BarcelonaBellaterraSpain

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