An algorithmic Solution in Data Visualization for the “Hair Ball” Problem

Abstract

Researching and analyzing large and complex graphs is an important aspect of data visualization research, but completely new, scalable methods and graph visualization methodologies are required [49]. Overall, this can provide more insight into this fuzzy graph's structure and function. To clarify further, in the “Hair Balls” we need to find a technique to build a solution for presenting a clean graph with the minimum overlap between edges. Despite the growing importance of researching and thoroughly examining and interpreting very large data graphs, the traditional way of viewing graphs has trouble scaling up, and usually ends up representing such large graphs as “Hair Balls.” Nevertheless, this traditional approach has a profoundly intuitive foundation [75]: nodes are represented in a form such as a circle, triangle or square, which are then bound by lines or curves representing the edges [73]. In any way, while there are many different methods of applying this fundamental underlying concept, it needs to be reconsidered in the given current and developing needs to consider the increasingly complex convergence between the edges in the graphs [55]. The Hair Ball complex, appearing as an indecipherable diagram, originated from the edge-to-edge convergence. We found the major drawback in the Hair Balls graph from our preliminary research was that it confused observers [38]–[40]. Users might feel that there are some extra nodes; but they don't actually exist. Since there are many crossovers in the Hair Balls between the edges, the impression can also affect observers ‘ understanding of the graph's entire structure [38] [39]. Major problem-no effective reception of information from a Hair Balls graph-meaningless to observers [64].