CMGV: a unified framework for complexity management in graph visualization

Abstract

In today’s era of technological revolution, the sheer volume of data being produced poses a significant challenge for analyzing relational data of such scale, particularly in terms of visual analysis. Graphs provide an effective way of organizing and representing relational data, with nodes representing entities. In contrast, edges representing relationships, a comprehensive and intuitive view of complex large-scale data is created. A well-represented visualization of complex graphs allows users to understand relationships, uncover new insights, and discover hid-den patterns. To this end, we introduce a complexity management framework for effectively analyzing large-scale relational data represented as graphs. Existing methods for managing graph complexity work independently and may lead to in-consistencies and confusion consecutively applied. The Complexity Management Graph Visualization framework (CMGV) presents a novel approach integrating commonly used complexity management techniques while ensuring the preservation of the user’s mental map through a specialized layout algorithm. The frame-work introduces an intuitive Graph Complexity Management Model (CMGM) for both graph representation and complexity management. CMGV supports commonly utilized complexity management tasks, including filtering, hiding, showing, collapsing, and expanding graph elements. Importantly, CMGV is designed to be independent of the rendering method and can be seamlessly integrated with different graph rendering libraries. This is possible through an extension that synchronizes the graph models between the rendering library and CMGM. Our experiments performed on randomly generated graphs verify that CMGV flawlessly performs consecutive graph complexity management operations, leaving the user graph intact, and outperforms existing complexity management solutions in terms of both runtime and generally accepted graph layout criteria. It is fast enough to be used in interactive applications with small to medium-sized graphs.