Comparison study on graph sampling algorithms for interactive visualizations of large-scale networks
Abstract: Networks are present in computer science, sociology, biology, and neuroscience as well as in applied fields such as transportation, communication, medical industries. The growing volumes of data collection are pushing scalability and performance requirements on graph algorithms, and at the same time, a need for a deeper understanding of these structures through visualization arises. Network diagrams or graph drawings can facilitate the understanding of data, making intuitive the identification of the largest clusters, the number of connected components, the overall structure, and detecting anomalies, which is not achievable through textual or matrix representations. The aim of this study was to evaluate approaches that would enable visualization of a large scale peer-to-peer video live streaming networks. The visualization of such large scale graphs has technical limitations which can be overcome by filtering important structural data from the networks. In this study, four sampling algorithms for graph reduction were applied to large overlay peer-to-peer network graphs and compared. The four algorithms cover different approaches: selecting links with the highest weight, selecting nodes with the highest cumulative weight, using betweenness centrality metrics, and constructing a focus-based tree. Through the evaluation process, it was discovered that the algorithm based on betweenness centrality approximation offers the best results. Finally, for each of the algorithms in comparison, their resulting sampled graphs were visualized using a forcedirected layout with a 2-step loading approach to depict their effect on the representation of the graphs.
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