Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation

• URL: http://arxiv.org/abs/2408.05456v2
• Date: Sun, 03 Aug 2025 07:59:43 GMT
• Title: Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation
• Authors: Wenbo Shang, Xuliang Zhu, Xin Huang,
• Abstract summary: We propose a novel Path-LLM model to efficiently learn unified graph representation.<n>Our framework consists of four well-designed techniques.<n>Compared with WalkLM, our approach saves more than 90% of training paths on millions-scale graphs and runs at most 35x faster.
• Score: 6.401420962078335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unified graph representation learning aims to generate node embeddings, which can be applied to multiple downstream applications of graph analytics. However, existing studies based on graph neural networks and language models either suffer from the limitations of numerous training needs toward specific downstream predictions, poor generalization, or shallow semantic features. In this work, we propose a novel Path-LLM model to efficiently learn unified graph representation, which leverages a powerful large language model (LLM) to incorporate our proposed path features. Our Path-LLM framework consists of four well-designed techniques. First, we develop a new mechanism of long-to-short shortest path (L2SP) selection, which can cover key connections between different dense groups. An in-depth analysis and comparison of different path selections is conducted to justify the rationale behind our designed L2SP method. Next, we design path textualization to obtain L2SP-based training texts with key phrase selection from node text attributes. We then feed the texts into a self-supervised LLM training process to align next node/edge generation in L2SP with next token generation in causal language modeling for graph representation learning and finally extract the unified graph embeddings. We theoretically analyze the algorithm complexity of our Path-LLM approach. Extensive experiments on large-scale graph benchmarks validate the superiority of Path-LLM against state-of-the-art methods WalkLM, GraphGPT, OFA, and GraphTranslator on two classical graph learning tasks (node classification and edge validation) and one NP-hard graph query processing task (keyword search). Compared with WalkLM, our approach saves more than 90% of training paths on millions-scale graphs and runs at most 35x faster.

Related papers

• Skeleton-Guided Learning for Shortest Path Search [31.51778160288742]
  Shortest path search is a core operation in graph-based applications.<n>We propose a versatile learning-based framework for shortest path search on generic graphs.
  arXiv  Detail & Related papers  (2025-08-04T10:21:52Z)
• Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks [23.07584018576066]
  We propose a SiMple yet effectIve approach for graph few-shot Learning with fEwer tasks, named SMILE. We introduce a dual-level mixup strategy, encompassing both within-task and across-task mixup, to simultaneously enrich the available nodes and tasks in meta-learning. Empirically, SMILE consistently outperforms other competitive models by a large margin across all evaluated datasets with in-domain and cross-domain settings.
  arXiv  Detail & Related papers  (2025-02-19T23:59:05Z)
• Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning [21.848367623218124]
  We present a review of methodologies for applying Large Language Models to graphs, termed LLM4graph.<n>The core of LLM4graph lies in transforming graphs into texts for LLMs to understand and analyze.<n>Existing methods can be divided into two paradigms: Graph2text and Graph2token.
  arXiv  Detail & Related papers  (2025-01-02T07:45:34Z)
• Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? [62.12375949429938]
  Building transferable Graph Neural Networks (GNNs) with CLIP pipeline is challenging because of three fundamental issues. We leverage multi-modal prompt learning to effectively adapt pre-trained GNN to downstream tasks and data. Our new paradigm embeds the graphs directly in the same space as the Large Language Models (LLMs) by learning both graph prompts and text prompts simultaneously.
  arXiv  Detail & Related papers  (2024-12-11T08:03:35Z)
• Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance [67.26434607115392]
  Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. LVLMs suffer from hallucinations caused by language bias, leading to diminished focus on images and ineffective visual comprehension. We propose LACING to address the language bias of LVLMs with muLtimodal duAl-attention meChanIsm (MDA) aNd soft-image Guidance (IFG)
  arXiv  Detail & Related papers  (2024-11-21T16:33:30Z)
• Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models [88.4320775961431]
  We introduce ProGraph, a benchmark for large language models (LLMs) to process graphs.<n>Our findings reveal that the performance of current LLMs is unsatisfactory, with the best model achieving only 36% accuracy.<n>We propose LLM4Graph datasets, which include crawled documents and auto-generated codes based on 6 widely used graph libraries.
  arXiv  Detail & Related papers  (2024-09-29T11:38:45Z)
• LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings [7.302176015732192]
  We introduce a novel framework named Token Embedding-Aligned Graph Language Model (TEA-GLM) We pretrain a GNN, aligning its representations with token embeddings of an LLM. We then train a linear projector that transforms the GNN's representations into a fixed number of graph token embeddings.
  arXiv  Detail & Related papers  (2024-08-25T04:32:45Z)
• SSP-GNN: Learning to Track via Bilevel Optimization [3.1889516673296807]
  We propose a graph-based tracking formulation for multi-object tracking (MOT) Our method applies a successive shortest paths (SSP) algorithm to a tracking graph defined over a batch of frames. The edge costs in this tracking graph are computed via a message-passing network, a graph neural network (GNN) variant.
  arXiv  Detail & Related papers  (2024-07-05T07:23:51Z)
• Can Graph Learning Improve Planning in LLM-based Agents? [61.47027387839096]
  Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs) In this paper, we explore graph learning-based methods for task planning, a direction that is to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs.
  arXiv  Detail & Related papers  (2024-05-29T14:26:24Z)
• Parameter-Efficient Tuning Large Language Models for Graph Representation Learning [62.26278815157628]
  We introduce Graph-aware. Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning. We use a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations.
  arXiv  Detail & Related papers  (2024-04-28T18:36:59Z)
• Test-Time Training on Graphs with Large Language Models (LLMs) [68.375487369596]
  Test-Time Training (TTT) has been proposed as a promising approach to train Graph Neural Networks (GNNs) Inspired by the great annotation ability of Large Language Models (LLMs) on Text-Attributed Graphs (TAGs), we propose to enhance the test-time training on graphs with LLMs as annotators. A two-stage training strategy is designed to tailor the test-time model with the limited and noisy labels.
  arXiv  Detail & Related papers  (2024-04-21T08:20:02Z)
• GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism [10.573861741540853]
  We propose a graph-guided self-attention mechanism, GraSAME, for pretrained language models. GraSAME seamlessly incorporates token-level structural information into PLMs without necessitating additional alignment or concatenation efforts. Our experiments on the graph-to-text generation task demonstrate that GraSAME outperforms baseline models and achieves results comparable to state-of-the-art (SOTA) models on WebNLG datasets.
  arXiv  Detail & Related papers  (2024-04-10T11:03:57Z)
• A Language Model based Framework for New Concept Placement in Ontologies [33.31096456574208]
  We investigate the task of inserting new concepts extracted from texts into an ontology using language models. We propose to leverage neural methods, where we apply embedding-based methods and contrastive learning. We evaluate the methods on recent datasets created using the SNOMED CT and the MedMentions entity linking benchmark.
  arXiv  Detail & Related papers  (2024-02-27T21:27:35Z)
• SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [131.04781590452308]
  We present SimTeG, a frustratingly Simple approach for Textual Graph learning. We first perform supervised parameter-efficient fine-tuning (PEFT) on a pre-trained LM on the downstream task. We then generate node embeddings using the last hidden states of finetuned LM.
  arXiv  Detail & Related papers  (2023-08-03T07:00:04Z)
• Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs [59.74814230246034]
  Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities. We investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors.
  arXiv  Detail & Related papers  (2023-07-07T05:31:31Z)
• Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking [6.510588721127479]
  Experimental results demonstrate that a simple online MPN with these two contributions can perform better than many state-of-the-art methods. Our association method generalizes well and can also improve the results of private detection based methods.
  arXiv  Detail & Related papers  (2022-12-06T14:10:57Z)
• Symbolic Learning to Optimize: Towards Interpretability and Scalability [113.23813868412954]
  Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks. Existing L2O models parameterize optimization rules by neural networks, and learn those numerical rules via meta-training. In this paper, we establish a holistic symbolic representation and analysis framework for L2O. We propose a lightweight L2O model that can be meta-trained on large-scale problems and outperformed human-designed and tuneds.
  arXiv  Detail & Related papers  (2022-03-13T06:04:25Z)
• Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking [58.30147362745852]
  Data association across frames is at the core of Multiple Object Tracking (MOT) task. Existing methods mostly ignore the context information among tracklets and intra-frame detections. We propose a novel learnable graph matching method to address these issues.
  arXiv  Detail & Related papers  (2021-03-30T08:58:45Z)

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.