mlsanityData to 14 Oct 2026

Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark

arXiv 2405.06634 ↗First posted 10 May 2024cs.CV cs.AI cs.CLcited by 2

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

We evaluate the zero-shot ability of GPT-4 and LLaVa to perform simple Visual Network Analysis (VNA) tasks on small-scale graphs. We evaluate the Vision Language Models (VLMs) on 5 tasks related to three foundational network science concepts: identifying nodes of maximal degree on a rendered graph, identifying whether signed triads are balanced or unbalanced, and counting components. The tasks are structured to be easy for a human who understands the underlying graph theoretic concepts, and can all be solved by counting the appropriate elements in graphs. We find that while GPT-4 consistently outperforms LLaVa, both models struggle with every visual network analysis task we propose. We publicly release the first benchmark for the evaluation of VLMs on foundational VNA tasks.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 9 of its 17 references that are in the map. First posted on arXiv 10 May 2024.

Builds on

9 of 17 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context8 May
  2. 2023Vision-Language Models for Vision Tasks: A Survey3 Apr

Other references · 7

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
  3. 2023Talk like a Graph: Encoding Graphs for Large Language Modelscited by 176
  4. 2024Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation datacited by 39
  5. 2024Large Language Models for Social Networks: Applications, Challenges, and Solutionscited by 11
  6. 2023Zero-Shot Object Counting with Language-Vision Modelscited by 9
  7. 2023Pushing Boundaries: Exploring Zero Shot Object Classification with Large Multimodal Modelscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Modelscited by 3
  2. 2026When Vision Meets Graphs: A Survey on Graph Reasoning and Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning
  2. 2026GraphVLM: Benchmarking Vision Language Models for Multimodal Graph Learning
  3. 2026GraphVerse: A Comprehensive Visual Graph Reasoning Benchmark for Multimodal Large Language Models
  4. 2025Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models
  5. 2024UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
  6. 2026MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph
  7. 2024Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning
  8. 2025Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models