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New method analyzes graph structures to benchmark shortest-path algorithms

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Researchers have developed a new method for benchmarking shortest-path algorithms by analyzing graph structures. This approach embeds graphs into a feature space and clusters them to identify regions of similar structure, which are then used to evaluate algorithm performance. The study examined Erdos-Renyi graphs, random geometric graphs, and road networks, testing algorithms like Dijkstra, bidirectional Dijkstra, and A*. Findings indicate that while graph generators create stable structural regions, performance similarity does not always align with structural similarity, and different benchmark families occupy distinct regions. AI

RANK_REASON The cluster contains a research paper detailing a new methodology for algorithm benchmarking. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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New method analyzes graph structures to benchmark shortest-path algorithms

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The cluster contains a research paper detailing a new methodology for algorithm benchmarking. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Maryam Gholami Shiri, Ivana Krminac, Marko Djukanovi\'c, Sa\v{s}o D\v{z}eroski, Eva Tuba, Tome Eftimov ·

    Graph Instance Landscapes: When Structural Similarity Does (Not) Reflect Shortest-Path Performance

    arXiv:2606.18267v1 Announce Type: cross Abstract: Benchmarking shortest-path algorithms is commonly based on aggregate performance over heterogeneous graph sets, which limits insight into how different search paradigms react to instance structure. We adopt an instance-landscape v…

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