Task-Centered Benchmark for Interactive Network Visualization & Analysis

2026-07-04

AI Summary

Existing graph systems lack systematic evaluation for interactive network visualization and analysis (INVA) tasks. This work proposes the first benchmarking framework specifically designed for INVA, employing a task-driven evaluation methodology, multi-backend comparative experiments, and modeling of interactive analysis workflows to comprehensively assess mainstream graph systems in terms of functionality, performance, and correctness. The study reveals significant performance gaps and latent deficiencies in current systems when deployed in INVA scenarios, offering empirical evidence and new research directions to guide future system optimization and development.

Abstract

Interactive network visualization and analysis (INVA) enables iterative, visual and algorithmic analysis of large network datasets. Although numerous benchmarks have been developed to evaluate different graph analysis algorithms and systems, we observe a lack of such efforts for interactive network data understanding. In this work, we address the question - How well do existing graph systems serve the purpose of Interactive Network Visualization and Analysis? To this end, we build and demonstrate the use of the first task-centered benchmarking framework to evaluate a variety of graph system backends on INVA workloads. Our benchmarking results highlight a gap between both the capabilities and performance of existing graph systems for INVA use cases, and uncover possible bugs in these systems. Based on our benchmarking results, we reveal new opportunities for research and development to better support interactive network visualization and analysis.

Problem

• interactive network visualization
• graph systems
• benchmarking
• INVA
• task-centered evaluation

Innovation

• interactive network visualization
• task-centered benchmark
• graph systems evaluation
• INVA workloads
• network data understanding