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To support this, visual analytics systems frequently employ quantitative computational measures to provide automated guidance. However, it remains unclear how well these measures align with subjective human visual perception, thereby offering recommendations that conflict with analysts\u2019 intuitive judgments, potentially leading to confusion rather than reducing cognitive load. Multimodal Large Language Models (MLLMs), capable of visually interpreting graphs and explaining their reasoning in natural language, have emerged as a potential alternative to address this challenge. This paper bridges the gap between human and machine assessment of graph similarity through 3 interconnected experiments using a dataset of 1881 node-link diagrams. Experiment 1 collects relative similarity judgments and rationales from 32 human participants, revealing consensus on graph similarity while prioritizing global shapes and edge densities over exact topological details. Experiment 2 benchmarks 16 computational measures against these human judgments, identifying Portrait divergence as the best-performing metric, though with only moderate alignment. Experiment 3 evaluates the potential of 3 state-of-the-art MLLMs (GPT-5, Gemini 2.5 Pro, Claude Sonnet 4.5) as perceptual proxies. The results demonstrate that MLLMs, particularly GPT-5, significantly outperform traditional measures in aligning with human graph similarity perception and provide interpretable rationales for their decisions, whereas Claude Sonnet 4.5 shows the best computational efficiency. Our findings suggest that MLLMs hold significant promise not only as effective, explainable proxies for human perception but also as intelligent guides that can uncover subtle nuances that might be overlooked by human analysts in visual analytics systems.<\/jats:p>","DOI":"10.1177\/14738716261434961","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:11:06Z","timestamp":1776154266000},"page":"333-357","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Seeing graphs like humans: Benchmarking computational measures and MLLMs for similarity assessment"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5326-765X","authenticated-orcid":false,"given":"Seokweon","family":"Jung","sequence":"first","affiliation":[{"name":"LLM Innovation Research Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea"},{"name":"Department of Computer Science and Engineering, Seoul National University, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3533-6603","authenticated-orcid":false,"given":"Jeongmin","family":"Rhee","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4905-1175","authenticated-orcid":false,"given":"Seoyoung","family":"Doh","sequence":"additional","affiliation":[{"name":"College of Liberal Studies, Seoul National University, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9659-2922","authenticated-orcid":false,"given":"Hyeon","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Seoul National University, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-5048","authenticated-orcid":false,"given":"Ghulam Jilani","family":"Quadri","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Oklahoma, Norman, OK, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7734-822X","authenticated-orcid":false,"given":"Jinwook","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Seoul National University, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,4,14]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","unstructured":"Assenov Y Ram\u00edrez F Schelhorn SE et al. 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