@inproceedings{shi-etal-2026-enhancing,
title = "Enhancing Dynamic Text-Attributed Graph Modeling via Instruction Tuning",
author = "Shi, Weili and
Li, Haoming and
Li, Sheng",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.67/",
pages = "1314--1329",
ISBN = "979-8-89176-444-6",
abstract = "Unlike conventional static graphs, dynamic graphs evolve over time as their links change. Recent benchmarks, such as the Dynamic Text-attributed Graph Benchmark, further enable the study of dynamic graph learning with rich textual information on nodes and edges. The success of graph instruction tuning on static text-attributed graphs motivates us to extend this approach to dynamic graphs. However, previous works suggest directly using LLMs as predictors on dynamic graphs often underperforms traditional dynamic graph models. To address this issue, we propose a novel two-stage training framework consisting of Dynamic Text-attributed Graph Instruction Tuning (DTGIT) followed by standard supervised training. Unlike prior approaches that employ instruction tuning to enhance LLMs' understanding of graph structures and directly use LLMs as predictors for downstream tasks, our proposed instruction tuning framework serves as a warm-up stage before standard supervised training. Specifically, during the instruction tuning stage, the embeddings produced by the dynamic graph model are aligned with temporal semantics in the natural language space, while the LLM itself is not directly used as a predictor for downstream tasks. In the second stage, the dynamic graph model is initialized with the knowledge acquired during instruction tuning and further fine-tuned in a supervised manner for downstream tasks. Extensive experiments demonstrate that existing dynamic graph models{~}(e.g., DyGFormer) can achieve consistent performance improvement on the downstream tasks, particularly on link prediction, when trained with our framework."
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<abstract>Unlike conventional static graphs, dynamic graphs evolve over time as their links change. Recent benchmarks, such as the Dynamic Text-attributed Graph Benchmark, further enable the study of dynamic graph learning with rich textual information on nodes and edges. The success of graph instruction tuning on static text-attributed graphs motivates us to extend this approach to dynamic graphs. However, previous works suggest directly using LLMs as predictors on dynamic graphs often underperforms traditional dynamic graph models. To address this issue, we propose a novel two-stage training framework consisting of Dynamic Text-attributed Graph Instruction Tuning (DTGIT) followed by standard supervised training. Unlike prior approaches that employ instruction tuning to enhance LLMs’ understanding of graph structures and directly use LLMs as predictors for downstream tasks, our proposed instruction tuning framework serves as a warm-up stage before standard supervised training. Specifically, during the instruction tuning stage, the embeddings produced by the dynamic graph model are aligned with temporal semantics in the natural language space, while the LLM itself is not directly used as a predictor for downstream tasks. In the second stage, the dynamic graph model is initialized with the knowledge acquired during instruction tuning and further fine-tuned in a supervised manner for downstream tasks. Extensive experiments demonstrate that existing dynamic graph models (e.g., DyGFormer) can achieve consistent performance improvement on the downstream tasks, particularly on link prediction, when trained with our framework.</abstract>
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%0 Conference Proceedings
%T Enhancing Dynamic Text-Attributed Graph Modeling via Instruction Tuning
%A Shi, Weili
%A Li, Haoming
%A Li, Sheng
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F shi-etal-2026-enhancing
%X Unlike conventional static graphs, dynamic graphs evolve over time as their links change. Recent benchmarks, such as the Dynamic Text-attributed Graph Benchmark, further enable the study of dynamic graph learning with rich textual information on nodes and edges. The success of graph instruction tuning on static text-attributed graphs motivates us to extend this approach to dynamic graphs. However, previous works suggest directly using LLMs as predictors on dynamic graphs often underperforms traditional dynamic graph models. To address this issue, we propose a novel two-stage training framework consisting of Dynamic Text-attributed Graph Instruction Tuning (DTGIT) followed by standard supervised training. Unlike prior approaches that employ instruction tuning to enhance LLMs’ understanding of graph structures and directly use LLMs as predictors for downstream tasks, our proposed instruction tuning framework serves as a warm-up stage before standard supervised training. Specifically, during the instruction tuning stage, the embeddings produced by the dynamic graph model are aligned with temporal semantics in the natural language space, while the LLM itself is not directly used as a predictor for downstream tasks. In the second stage, the dynamic graph model is initialized with the knowledge acquired during instruction tuning and further fine-tuned in a supervised manner for downstream tasks. Extensive experiments demonstrate that existing dynamic graph models (e.g., DyGFormer) can achieve consistent performance improvement on the downstream tasks, particularly on link prediction, when trained with our framework.
%U https://aclanthology.org/2026.findings-aacl.67/
%P 1314-1329
Markdown (Informal)
[Enhancing Dynamic Text-Attributed Graph Modeling via Instruction Tuning](https://aclanthology.org/2026.findings-aacl.67/) (Shi et al., Findings 2026)
ACL