Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning

• URL: http://arxiv.org/abs/2410.12096v1
• Date: Tue, 15 Oct 2024 22:43:32 GMT
• Title: Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning
• Authors: Guangxin Su, Yifan Zhu, Wenjie Zhang, Hanchen Wang, Ying Zhang,
• Abstract summary: Graph representation learning is crucial for real-world applications but often encounters pervasive noise. We introduce LangGSL, a framework that integrates the complementary strengths of pre-trained language models and graph structure learning models.
• Score: 22.993015048941444
• License:
• Abstract: Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art methods typically address noise by focusing separately on node features with large language models (LLMs) and on graph structures with graph structure learning models (GSLMs). In this paper, we introduce LangGSL, a robust framework that integrates the complementary strengths of pre-trained language models and GSLMs to jointly enhance both node feature and graph structure learning. In LangGSL, we first leverage LLMs to filter noise in the raw data and extract valuable cleaned information as features, enhancing the synergy of downstream models. During the mutual learning phase in LangGSL, the core idea is to leverage the relatively small language model (LM) to process local attributes and generate reliable pseudo-labels and informative node embeddings, which are then integrated into the GSLM's prediction phase. This approach enriches the global context and enhances overall performance. Meanwhile, GSLM refines the evolving graph structure constructed from the LM's output, offering updated labels back to the LM as additional guidance, thus facilitating a more effective mutual learning process. The LM and GSLM work synergistically, complementing each other's strengths and offsetting weaknesses within a variational information-maximizing framework, resulting in enhanced node features and a more robust graph structure. Extensive experiments on diverse graph datasets of varying scales and across different task scenarios demonstrate the scalability and effectiveness of the proposed approach.

Related papers

• GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion [52.026016846945424]
  We propose a new method called GLTW, which encodes the structural information of KGs and merges it with Large Language Models. Specifically, we introduce an improved Graph Transformer (iGT) that effectively encodes subgraphs with both local and global structural information. Also, we develop a subgraph-based multi-classification training objective, using all entities within KG as classification objects, to boost learning efficiency.
  arXiv  Detail & Related papers  (2025-02-17T06:02:59Z)
• Deep Semantic Graph Learning via LLM based Node Enhancement [5.312946761836463]
  Large Language Models (LLMs) have demonstrated superior capabilities in understanding text semantics. This paper proposes a novel framework that combines Graph Transformer architecture with LLM-enhanced node features.
  arXiv  Detail & Related papers  (2025-02-11T21:55:46Z)
• NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models [26.739650151993928]
  Graphs are a fundamental data structure for representing relationships in real-world scenarios. Applying Large Language Models (LLMs) to graph-related tasks poses significant challenges. We introduce Node Tokenizer for Large Language Models (NT-LLM), a novel framework that efficiently encodes graph structures.
  arXiv  Detail & Related papers  (2024-10-14T17:21:57Z)
• How to Make LLMs Strong Node Classifiers? [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs) We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art (SOTA) GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks [51.19110891434727]
  Large Language Models (LLMs) with pretrained knowledge and powerful semantic comprehension abilities have recently shown a remarkable ability to benefit applications using vision and text data. E-LLaGNN is a framework with an on-demand LLM service that enriches message passing procedure of graph learning by enhancing a limited fraction of nodes from the graph.
  arXiv  Detail & Related papers  (2024-07-20T22:09:42Z)
• Multi-View Empowered Structural Graph Wordification for Language Models [12.22063024099311]
  We introduce an end-to-end modality-aligning framework for LLM-graph alignment: Dual-Residual Vector Quantized-Variational AutoEncoder, namely Dr.E. Our approach is purposefully designed to facilitate token-level alignment with LLMs, enabling an effective translation of the intrinsic'of graphs into comprehensible natural language. Our framework ensures certain visual interpretability, efficiency, and robustness, marking the promising successful endeavor to achieve token-level alignment between LLMs and GNNs.
  arXiv  Detail & Related papers  (2024-06-19T16:43:56Z)
• LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modeling [10.907949155931474]
  We introduce LangTopo, which aligns graph structure modeling with natural language understanding at the token level. We demonstrate the effectiveness of our proposed method on multiple datasets.
  arXiv  Detail & Related papers  (2024-06-19T06:20:22Z)
• GraphEdit: Large Language Models for Graph Structure Learning [62.618818029177355]
  Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data. Existing GSL methods heavily depend on explicit graph structural information as supervision signals. We propose GraphEdit, an approach that leverages large language models (LLMs) to learn complex node relationships in graph-structured data.
  arXiv  Detail & Related papers  (2024-02-23T08:29:42Z)
• Contextualization Distillation from Large Language Model for Knowledge Graph Completion [51.126166442122546]
  We introduce the Contextualization Distillation strategy, a plug-in-and-play approach compatible with both discriminative and generative KGC frameworks. Our method begins by instructing large language models to transform compact, structural triplets into context-rich segments. Comprehensive evaluations across diverse datasets and KGC techniques highlight the efficacy and adaptability of our approach.
  arXiv  Detail & Related papers  (2024-01-28T08:56:49Z)
• Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs [57.052160123387104]
  We present the Disentangled Graph-Text Learner (DGTL) model, which is able to enhance the reasoning and predicting capabilities of LLMs for TAGs. Our proposed DGTL model incorporates graph structure information through tailored disentangled graph neural network (GNN) layers. Experimental evaluations demonstrate the effectiveness of the proposed DGTL model on achieving superior or comparable performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-10-27T14:00:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.