mlsanityData to 14 Oct 2026

When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning

arXiv 2312.10372 ↗First posted 16 Dec 2023cs.AIcited by 2

On the map: LLM reasoning → Multimodal reasoning

Abstract (arXiv)

Graph data is ubiquitous in the physical world, and it has always been a challenge to efficiently model graph structures using a unified paradigm for the understanding and reasoning on various graphs. Moreover, in the era of large language models, integrating complex graph information into text sequences has become exceptionally difficult, which hinders the ability to interact with graph data through natural language instructions.The paper presents a new paradigm for understanding and reasoning about graph data by integrating image encoding and multimodal technologies. This approach enables the comprehension of graph data through an instruction-response format, utilizing GPT-4V's advanced capabilities. The study evaluates this paradigm on various graph types, highlighting the model's strengths and weaknesses, particularly in Chinese OCR performance and complex reasoning tasks. The findings suggest new direction for enhancing graph data processing and natural language interaction.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 18 of its 30 references that are in the map. First posted on arXiv 16 Dec 2023.

Builds on

18 of 30 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  6. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  7. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
  8. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  9. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  10. 2018Link Prediction Based on Graph Neural Networkscited by 832
  11. 2015Gated Graph Sequence Neural Networkscited by 796
  12. 2019Heterogeneous Graph Attention Networkcited by 734
6 more
  1. 2019Graph Neural Networks for Social Recommendationcited by 714
  2. 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 681
  3. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  4. 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
  5. 2023GraphLLM: Boosting Graph Reasoning Ability of Large Language Modelcited by 107
  6. 2022Zero-Shot Rumor Detection with Propagation Structure via Prompt Learningcited by 32

Built on by

Cited by year

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

2 papers.

  1. 2024A Survey of Large Language Models for Graphscited by 72
  2. 2025Teaching Language Models to Reason with Toolscited by 5

Similar, not cited

Close in wording, no citation either way

  1. 2024Exploring Graph Structure Comprehension Ability of Multimodal Large Language Models: Case Studies
  2. 2025GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs
  3. 2023Which Modality should I use -- Text, Motif, or Image? : Understanding Graphs with Large Language Models
  4. 2026GraphVerse: A Comprehensive Visual Graph Reasoning Benchmark for Multimodal Large Language Models
  5. 2024VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
  6. 2025Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models
  7. 2025MLaGA: Multimodal Large Language and Graph Assistant
  8. 2024GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning