Organizations: Beijing University of Posts and Telecommunications, Beijing, China · Beihang University, Beijing, China · China Telecom Bestpay, Beijing, China
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph-related tasks, with the ultimate goal of developing a graph foundation model that generalizes diverse scenarios. The key challenge is to align graph data with language spaces so that LLMs can better comprehend graphs. As a popular paradigm, Graph-Tokenizing LLMs (GTokenLLMs) encode complex structures and lengthy texts into a graph token sequence, and then align them with text tokens via language instructions tuning. Despite their initial success, our information-theoretic analysis reveals that existing GTokenLLMs rely solely on text supervision from language instructions, which achieve only implicit graph-text alignment, resulting in a text-dominant bias that underutilizes graph context. To overcome this limitation, we first prove that the alignment objective is upper-bounded by the mutual information between the input graphs and their hidden representations in the LLM, which motivates us to improve this upper bound to achieve better alignment. To this end, we further propose a reconstructive graph instruction tuning pipeline, RGLM. Our key idea is to reconstruct the graph information from the LLM's graph token outputs, explicitly incorporating graph supervision to constrain the alignment process. Technically, we embody RGLM by exploring three distinct variants from two complementary perspectives: RGLM-Decoder from the input space; RGLM-Similarizer and RGLM-Denoiser from the latent space. Additionally, we theoretically analyze the alignment effectiveness of each variant. Extensive experiments on various benchmarks and task scenarios validate the effectiveness of the proposed RGLM, paving the way for new directions in GTokenLLMs' alignment research.
Figures & tables
Figure 1. (a) Typical graph instruction tuning methods follow an LLM-centric design that solely leverages text supervision. (b) RGLM designs extra reconstructive graph tasks to explicitly incorporate graph supervision into the tuning process.
Figure 2. Neighbor detail template for graph serialization ( Chen et al., 2024b ) .
Figure 3. (a) Overview of RGLM. Given an input TAG, RGLM aims to reconstruct the graph information from the LLM’s graph token outputs, explicitly incorporating graph supervision to constrain the alignment process. (b) Decoder variant directly reconstructs the raw node features and topology in the input space. (c) Similarizer and (d) Denoiser variants reconstruct latent node representations of pre-trained GNNs via the cosine-similarity and denoising strategy, respectively.
Method
Dataset
Cora
Pubmed
OGBN-Arxiv
Reddit
Rank
Metric
Acc
F1
Acc
F1
Acc
F1
Acc
F1
Acc
F1
GNNs
GCN
89.30
87.95
89.48
89.04
73.83
54.97
63.19
62.49
5
6
GraphSAGE
87.82
86.77
90.54
90.51
74.47
56.41
58.51
58.41
10
8
GAT
87.08
86.32
88.11
87.60
74.39
57.71
61.78
65.38
9
5
DIFFormer
85.60
84.31
81.60
81.94
58.80
42.44
55.30
55.09
15
14
UniMP
87.82
86.32
90.34
90.17
70.96
53.24
59.55
59.53
11
10
Table 1. Node classification results under supervised settings. Best are bolded, and runner-up are underlined. “OOM” indicates out-of-memory, exceeding the A800’s 80 GB limit. “Rank” represents the overall ranking on all datasets.
Figure 4. Ablation study on the node classification task across different datasets, where accuracy is reported for each variant.
Figure 5. Ablation study on the link prediction task across different datasets, where accuracy is reported for each variant.
Method
Dataset
Cora
Pubmed
Reddit
Metric
Acc
F1
Acc
F1
Acc
F1
Baselines
GCN
84.26
83.91
90.00
89.98
74.38
73.85
UniMP
71.62
70.92
80.76
80.54
73.09
72.53
GRACE
70.44
69.19
81.74
81.56
61.68
60.88
GraphGPT
59.85
59.85
73.60
73.39
56.69
27.19
LLaGA
82.21
79.66
91.78
91.88
79.73
76.60
Table 2. Link prediction results under supervised settings. Best are bolded, and runner-up are underlined.
Method
Training set
Cora+Pubmed
Test set
Cora
Pubmed
Metric
Acc
F1
Acc
F1
Baselines
GCN
85.61
84.16
85.07
84.29
GraphSAGE
86.35
86.21
89.27
89.12
GAT
83.03
82.29
86.51
85.99
UniMP
86.35
85.21
89.20
89.14
Table 3. Node classification results under multi-dataset training. Best are bolded, and runner-up are underlined.
Figure 6. Zero-shot accuracy on node classification and link prediction tasks, where models are trained on the OGBN-Arxiv and evaluated on unseen Cora and Pubmed.
Figure 7. Attention distribution over graph tokens for different methods, where the attention scores are log-scaled.
Figure 8. Time complexity (left) and resource cost (right) analysis of different RGLM variants.
Figure 9. Compatibility analysis of different RGLM variants on base LLMs, where accuracy is reported for each variant.
Figure 10. Analysis of the hyper-parameter λs and λf in the RGLM-Decoder variant.
Figure 11. Analysis of the hyper-parameter λl in the RGLM-Similarizer and RGLM-Denoiser variants.