In this report, we present TAGLAS, an atlas of text-attributed graph (TAG)
datasets and benchmarks. TAGs are graphs with node and edge features
represented in text, which have recently gained wide applicability in training
graph-language or graph foundation models. In TAGLAS, we collect and integrate
more than 23 TAG datasets with domains ranging from citation graphs to molecule
graphs and tasks from node classification to graph question-answering. Unlike
previous graph datasets and benchmarks, all datasets in TAGLAS have a unified
node and edge text feature format, which allows a graph model to be
simultaneously trained and evaluated on multiple datasets from various domains.
Further, we provide a standardized, efficient, and simplified way to load all
datasets and tasks. We also provide useful utils like text-to-embedding
conversion, and graph-to-text conversion, which can facilitate different
evaluation scenarios. Finally, we also provide standard and easy-to-use
evaluation utils. The project is open-sourced at
this https URL and is still under construction. Please
expect more datasets/features in the future.

TAGLAS 是一个包含文字属性图数据集和基准测试的图谱，其中包括超过 23 个 TAG 数据集，涵盖引文图到分子图的不同领域和节点分类到图问题回答的不同任务。TAGLAS 的数据集具有统一的节点和边缘文字特征格式，允许在不同领域的多个数据集上同时训练和评估图模型。此项目还提供了标准化、高效和简化的数据集和任务加载方式，以及有用的工具，如文字到嵌入式转换和图到文字转换，以促进不同的评估场景。该项目目前还在建设中，未来将提供更多数据集和特性。

TAGLAS: 在大规模图和语言模型时代的文本属性图数据集图集

TAGLAS: An atlas of text-attributed graph datasets in the era of large  graph and language models

Large language models (LLMs), while exhibiting exceptional performance,
suffer from hallucinations, especially on knowledge-intensive tasks. Existing
works propose to augment LLMs with individual text units retrieved from
external knowledge corpora to alleviate the issue. However, in many domains,
texts are interconnected (e.g., academic papers in a bibliographic graph are
linked by citations and co-authorships) which form a (text-attributed) graph.
The knowledge in such graphs is encoded not only in single texts/nodes but also
in their associated connections. To facilitate the research of augmenting LLMs
with graphs, we manually construct a Graph Reasoning Benchmark dataset called
GRBench, containing 1,740 questions that can be answered with the knowledge
from 10 domain graphs. Then, we propose a simple and effective framework called
Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging
LLMs to reason on the graph iteratively. Each Graph-CoT iteration consists of
three sub-steps: LLM reasoning, LLM-graph interaction, and graph execution. We
conduct systematic experiments with three LLM backbones on GRBench, where
Graph-CoT outperforms the baselines consistently. The code is available at
this https URL

使用 Graph-CoT 框架，通过对图进行迭代推理，将大型语言模型（LLMs）与图相结合，从而提高知识密集型任务中的性能。