TL;DR
This paper introduces embedded-graphs, a novel graph type using distributed representations to model complex relations, including linguistic ones, with new mathematical definitions and transformation methods.
Contribution
It proposes the concept of embedded-graphs, defines their mathematical properties, and presents transformation techniques to relate them to existing graph types.
Findings
Embedded-graphs can express complex linguistic relations.
Transformation methods connect embedded-graphs to weighted-graphs.
Examples and data structures illustrate the new graph type.
Abstract
In this paper, we propose a new type of graph, denoted as "embedded-graph", and its theory, which employs a distributed representation to describe the relations on the graph edges. Embedded-graphs can express linguistic and complicated relations, which cannot be expressed by the existing edge-graphs or weighted-graphs. We introduce the mathematical definition of embedded-graph, translation, edge distance, and graph similarity. We can transform an embedded-graph into a weighted-graph and a weighted-graph into an edge-graph by the translation method and by threshold calculation, respectively. The edge distance of an embedded-graph is a distance based on the components of a target vector, and it is calculated through cosine similarity with the target vector. The graph similarity is obtained considering the relations with linguistic complexity. In addition, we provide some examples and data…
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Taxonomy
TopicsComplex Network Analysis Techniques · Algorithms and Data Compression · Advanced Graph Neural Networks