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

Abstract

The investigation and analysis of large and complex graphs is an important aspect of data visualization research, yet there is a need for entirely new, scalable approaches and methodologies for graph visualization. This can ultimately provide more insight into the structure and function of this complex graph (Hair Ball). To explain more, we need to find a methodology to develop a solution to present a “Tidy” graph with the minimal crossover between edges in the “Hair Balls.” In spite of the expanding significance of investigating and extensively analyzing and understanding very large graphs of data, the traditional way of visualizing graphs has difficulties scaling up, and typically ends up depicting these large graphs as “Hair Balls”. This traditional approach does indeed have a deeply intuitive foundation: nodes are depicted with a shape such as a circle, triangle or square, which are then connected by lines or curves that represent the edges. In any case, although there are many different ways to apply this basic underlying idea, it needs to be revisited in light of current and emerging needs for understanding increasingly complex crossover between edges in the graphs. The complex “Hair Ball,” which appears as an indecipherable graph, came from the crossover between edges. From our preliminary research, we found the major disadvantage in the Hair Balls graph was that it confused observers. Users may think there are some extra nodes; but in reality, there are not. Because there are many crossovers between edges in the Hair Balls, the impression also may affect observers’ understanding of the whole structure of the graph. Major problem-no effective reception of information from a “Hair Balls” graph-meaningless to observers.