LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modeling

• URL: http://arxiv.org/abs/2406.13250v1
• Date: Wed, 19 Jun 2024 06:20:22 GMT
• Title: LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modeling
• Authors: Zhong Guan, Hongke Zhao, Likang Wu, Ming He, Jianpin Fan,
• Abstract summary: 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.
• Score: 10.907949155931474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, large language models (LLMs) have been widely researched in the field of graph machine learning due to their outstanding abilities in language comprehension and learning. However, the significant gap between natural language tasks and topological structure modeling poses a nonnegligible challenge. Specifically, since natural language descriptions are not sufficient for LLMs to understand and process graph-structured data, fine-tuned LLMs perform even worse than some traditional GNN models on graph tasks, lacking inherent modeling capabilities for graph structures. Existing research overly emphasizes LLMs' understanding of semantic information captured by external models, while inadequately exploring graph topological structure modeling, thereby overlooking the genuine capabilities that LLMs lack. Consequently, in this paper, we introduce a new framework, LangTopo, which aligns graph structure modeling with natural language understanding at the token level. LangTopo quantifies the graph structure modeling capabilities of GNNs and LLMs by constructing a codebook for the graph modality and performs consistency maximization. This process aligns the text description of LLM with the topological modeling of GNN, allowing LLM to learn the ability of GNN to capture graph structures, enabling LLM to handle graph-structured data independently. We demonstrate the effectiveness of our proposed method on multiple datasets.

Related papers

• A Hierarchical Language Model For Interpretable Graph Reasoning [47.460255447561906]
  We introduce Hierarchical Language Model for Graph (HLM-G), which employs a two-block architecture to capture node-centric local information and interaction-centric global structure. The proposed scheme allows LLMs to address various graph queries with high efficacy, efficiency, and robustness, while reducing computational costs on large-scale graph tasks. Comprehensive evaluations across diverse graph reasoning and real-world tasks of node, link, and graph-levels highlight the superiority of our method.
  arXiv  Detail & Related papers  (2024-10-29T00:28:02Z)
• Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning [22.993015048941444]
  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.
  arXiv  Detail & Related papers  (2024-10-15T22:43:32Z)
• 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)
• Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning [28.660326096652437]
  We introduce AskGNN, a novel approach that bridges the gap between sequential text processing and graph-structured data. AskGNN employs a Graph Neural Network (GNN)-powered structure-enhanced retriever to select labeled nodes across graphs. Experiments across three tasks and seven LLMs demonstrate AskGNN's superior effectiveness in graph task performance.
  arXiv  Detail & Related papers  (2024-10-09T17:19:12Z)
• How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension [53.6373473053431]
  This work introduces a benchmark to assess large language models' capabilities in graph pattern tasks. We have developed a benchmark that evaluates whether LLMs can understand graph patterns based on either terminological or topological descriptions. Our benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models.
  arXiv  Detail & Related papers  (2024-10-04T04:48:33Z)
• 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)
• Exploring the Potential of Large Language Models in Graph Generation [51.046188600990014]
  Graph generation requires large language models (LLMs) to generate graphs with given properties. This paper explores the abilities of LLMs for graph generation with systematical task designs and experiments. Our evaluations demonstrate that LLMs, particularly GPT-4, exhibit preliminary abilities in graph generation tasks.
  arXiv  Detail & Related papers  (2024-03-21T12:37:54Z)
• LLaGA: Large Language and Graph Assistant [73.71990472543027]
  Large Language and Graph Assistant (LLaGA) is an innovative model to handle the complexities of graph-structured data. LLaGA excels in versatility, generalizability and interpretability, allowing it to perform consistently well across different datasets and tasks. Our experiments show that LLaGA delivers outstanding performance across four datasets and three tasks using one single model.
  arXiv  Detail & Related papers  (2024-02-13T02:03:26Z)
• 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)
• Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data [13.524529952170672]
  Large language models (LLMs) have achieved impressive performance on many natural language processing tasks. We aim to assess whether LLMs can effectively process graph data and leverage topological structures to enhance performance. By comparing LLMs' performance with specialized graph models, we offer insights into the strengths and limitations of employing LLMs for graph analytics.
  arXiv  Detail & Related papers  (2023-10-07T23:25:22Z)

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.