Research on graph representation learning (a.k.a. embedding) has received great attention in recent years and shows effective results for various types of networks. Nevertheless, few initiatives have been focused on t...
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Research on graph representation learning (a.k.a. embedding) has received great attention in recent years and shows effective results for various types of networks. Nevertheless, few initiatives have been focused on the particular case of embeddings for bipartite graphs. In this paper, we first define the graph embedding problem in the case of bipartite graphs. Next, we propose a taxonomy of approaches used to tackle this problem and draw a description of state-of-the-art methods. Then, we establish their pros and cons with respect to conventional network embeddings. Finally, we provide a description of available resources to lead experiments on the subject.
About ten years ago, a novel graph edit distance framework based on bipartite graph matching has been introduced. This particular framework allows the approximation of graph edit distance in cubic time. This, in turn,...
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ISBN:
(纸本)9783319589619;9783319589602
About ten years ago, a novel graph edit distance framework based on bipartite graph matching has been introduced. This particular framework allows the approximation of graph edit distance in cubic time. This, in turn, makes the concept of graph edit distance also applicable to larger graphs. In the last decade the corresponding paper has been cited more than 360 times. Besides various extensions from the methodological point of view, we also observe a great variety of applications that make use of the bipartite graph matching framework. The present paper aims at giving a first survey on these applications stemming from six different categories (which range from document analysis, over biometrics to malware detection).
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