Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition

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• 关键词：Graphics recognition ; Graph classification ; Graph clustering ; Explicit graph embedding ; Topological node features ; Morgan index
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：9657
• 期：1
• 页码：117-132
• 丛书名：Graphic Recognition. Current Trends and Challenges
• ISBN：978-3-319-52159-6
• 卷排序：9657

文摘

The graphics recognition research community has been employing graphs, in one form or another, for at-least the last three decades. These data-structures have proven to be the most powerful representations for encoding the structural information of underlying data, for further processing. However, there is still a lack of tools and methods which could be employed to process these useful data-structures in an efficient manner. Graph embedding provides a solution for this problem. In this paper we present an improvement of the Fuzzy Multilevel Graph Embedding (FMGE) technique, by adding new topological node features, named Morgan Index. The experimental results on GREC, Mutagenicity and Fingerprint datasets from IAM graph database, illustrate improved results for the graph classification and graph clustering problems.