Browsing by Subject "Graph visualization software"

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Open Access
CMGV: a unified framework for complexity management in graph visualization
(2023-08) Zafar, Osama
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.
Open Access
HySE: a spring embedder approach for layout of hybrid graphs
(2023-09) Islam, Hamza
In recent times, the growth of data has been exponential, making the visual analysis of relational data progressively complex. Presenting such data in a visually appealing manner can help simplify the analysis process. Hybrid graphs, comprising a central directed or hierarchical part and interconnected undirected components, offer a practical structure for representing relational data with varying levels of abstraction while managing its complexity. To comprehend the relationships in data, discover insights, and get important patterns, a well-optimized graph layout for such graphs is needed. In response, we present HySE (Hybrid Spring Embedder), a novel graph layout algorithm tailored for hybrid graphs. HySE makes use of a holistic approach based on the popular spring embedder to achieve the aesthetics and quality of an optimized force-directed layout, not only on the undirected part of the graph but also on the hierarchy while maintaining the cohesion between both directed and undirected elements of the graph. The layout algorithm assumes the rank information of directed graph elements is already calculated with one of the popular approaches. Then, it finds appropriate initial positions and uses a force-directed layout technique to integrate the undirected parts into the layout, applying spring forces to model the edges, and repulsive electric forces for the nodes. Iteratively, HySE converges to an equilibrium state with minimized energy, resulting in visually pleasing and interpretable layouts for intricate hybrid graphs. Experiments performed on graphs, generated randomly through a well-designed process, validate that HySE performs as well as the state-of-the-art algorithms in terms of quality. It also matches the speed of well-established algorithms as well in small-to-medium-sized graphs.