# Concise Fuzzy Planar Embedding of Graphs: a Dimensionality Reduction Approach

## TL;DR

This paper introduces a fuzzy embedding method for large graphs that reduces dimensionality to save memory, enabling efficient neighbor queries with acceptable accuracy, especially in 2D space, supported by promising experimental results.

## Contribution

It proposes a novel fuzzy graph embedding technique that maps large graphs into low-dimensional Euclidean space, balancing memory efficiency and query accuracy.

## Key findings

- High accuracy in 2D embeddings demonstrated
- Memory savings achieved through dimensionality reduction
- Effective for neighbor queries with fuzzy logic support

## Abstract

The enormous amount of data to be represented using large graphs exceeds in some cases the resources of a conventional computer. Edges in particular can take up a considerable amount of memory as compared to the number of nodes. However, rigorous edge storage might not always be essential to be able to draw the needed conclusions. A similar problem takes records with many variables and attempts to extract the most discernible features. It is said that the ``dimension'' of this data is reduced. Following an approach with the same objective in mind, we can map a graph representation to a $k$-dimensional space and answer queries of neighboring nodes mainly by measuring Euclidean distances. The accuracy of our answers would decrease but would be compensated for by fuzzy logic which gives an idea about the likelihood of error. This method allows for reasonable representation in memory while maintaining a fair amount of useful information, and allows for concise embedding in $k$-dimensional Euclidean space as well as solving some problems without having to decompress the graph. Of particular interest is the case where $k=2$. Promising highly accurate experimental results are obtained and reported.