TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs

• URL: http://arxiv.org/abs/2406.10310v1
• Date: Fri, 14 Jun 2024 06:22:47 GMT
• Title: TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs
• Authors: Zhuofeng Li, Zixing Gou, Xiangnan Zhang, Zhongyuan Liu, Sirui Li, Yuntong Hu, Chen Ling, Zheng Zhang, Liang Zhao,
• Abstract summary: Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections. Existing TAG datasets predominantly feature textual information only at the nodes, with edges typically represented by mere binary or categorical attributes. To address this gap, we introduce Textual-Edge Graphs datasets featuring rich textual descriptions on nodes and edges.
• Score: 14.437863803271808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-world settings. However, existing TAG datasets predominantly feature textual information only at the nodes, with edges typically represented by mere binary or categorical attributes. This lack of rich textual edge annotations significantly limits the exploration of contextual relationships between entities, hindering deeper insights into graph-structured data. To address this gap, we introduce Textual-Edge Graphs Datasets and Benchmark (TEG-DB), a comprehensive and diverse collection of benchmark textual-edge datasets featuring rich textual descriptions on nodes and edges. The TEG-DB datasets are large-scale and encompass a wide range of domains, from citation networks to social networks. In addition, we conduct extensive benchmark experiments on TEG-DB to assess the extent to which current techniques, including pre-trained language models, graph neural networks, and their combinations, can utilize textual node and edge information. Our goal is to elicit advancements in textual-edge graph research, specifically in developing methodologies that exploit rich textual node and edge descriptions to enhance graph analysis and provide deeper insights into complex real-world networks. The entire TEG-DB project is publicly accessible as an open-source repository on Github, accessible at https://github.com/Zhuofeng-Li/TEG-Benchmark.

Related papers

• Bridging Local Details and Global Context in Text-Attributed Graphs [62.522550655068336]
  GraphBridge is a framework that bridges local and global perspectives by leveraging contextual textual information. Our method achieves state-of-theart performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues.
  arXiv  Detail & Related papers  (2024-06-18T13:35:25Z)
• DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs [28.340416573162898]
  Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios. Despite their broad applicability, there is a notable scarcity of benchmark datasets tailored to DyTAGs. We introduce Dynamic Text-attributed Graph Benchmark (DTGB), a collection of large-scale, time-evolving graphs.
  arXiv  Detail & Related papers  (2024-06-17T20:16:12Z)
• Hierarchical Compression of Text-Rich Graphs via Large Language Models [63.75293588479027]
  Text-rich graphs are prevalent in data mining contexts like e-commerce and academic graphs. This paper introduces Hierarchical Compression'' (HiCom), a novel method to align the capabilities of LLMs with the structure of text-rich graphs. HiCom can outperform both GNNs and LLM backbones for node classification on e-commerce and citation graphs.
  arXiv  Detail & Related papers  (2024-06-13T07:24:46Z)
• Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models [31.443478448031886]
  RoSE (Relation-oriented Semantic Edge-decomposition) is a novel framework that decomposes the graph structure by analyzing raw text attributes. Our framework significantly enhances node classification performance across various datasets, with improvements of up to 16% on the Wisconsin dataset.
  arXiv  Detail & Related papers  (2024-05-28T20:54:47Z)
• TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations [15.873944819608434]
  Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions. This paper introduces a new self-supervised learning framework, Text-And-Graph Multi-View Alignment (TAGA), which integrates TAGs' structural and semantic dimensions. Our framework demonstrates strong performance in zero-shot and few-shot scenarios across eight real-world datasets.
  arXiv  Detail & Related papers  (2024-05-27T03:40:16Z)
• Pretraining Language Models with Text-Attributed Heterogeneous Graphs [28.579509154284448]
  We present a new pretraining framework for Language Models (LMs) that explicitly considers the topological and heterogeneous information in Text-Attributed Heterogeneous Graphs (TAHGs) We propose a topology-aware pretraining task to predict nodes involved in the context graph by jointly optimizing an LM and an auxiliary heterogeneous graph neural network. We conduct link prediction and node classification tasks on three datasets from various domains.
  arXiv  Detail & Related papers  (2023-10-19T08:41:21Z)
• TeKo: Text-Rich Graph Neural Networks with External Knowledge [75.91477450060808]
  We propose a novel text-rich graph neural network with external knowledge (TeKo) We first present a flexible heterogeneous semantic network that incorporates high-quality entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
  arXiv  Detail & Related papers  (2022-06-15T02:33:10Z)
• GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [53.70520466556453]
  We propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph.
  arXiv  Detail & Related papers  (2021-05-06T12:20:41Z)
• Open Graph Benchmark: Datasets for Machine Learning on Graphs [86.96887552203479]
  We present the Open Graph Benchmark (OGB) to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics.
  arXiv  Detail & Related papers  (2020-05-02T03:09:50Z)
• Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs [63.12058935995516]
  Recent graph-to-text models generate text from graph-based data using either global or local aggregation. We propose novel neural models which encode an input graph combining both global and local node contexts. Our approaches lead to significant improvements on two graph-to-text datasets.
  arXiv  Detail & Related papers  (2020-01-29T18:24:14Z)

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.