@inproceedings{wang-etal-2024-bridging-local,
title = "Bridging Local Details and Global Context in Text-Attributed Graphs",
author = "Wang, Yaoke and
Zhu, Yun and
Zhang, Wenqiao and
Zhuang, Yueting and
Liyunfei, Liyunfei and
Tang, Siliang",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.823",
pages = "14830--14841",
abstract = "Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information. Research in this field generally consist of two main perspectives: local-level encoding and global-level aggregating, respectively refer to textual node information unification ($e.g.$, using Language Models) and structure-augmented modeling ($e.g.$, using Graph Neural Networks). Most existing works focus on combining different information levels but overlook the interconnections, $i.e.$, the contextual textual information among nodes, which provides semantic insights to bridge local and global levels. In this paper, we propose GraphBridge, a $multi-granularity integration$ framework that bridges local and global perspectives by leveraging contextual textual information, enhancing fine-grained understanding of TAGs. Besides, to tackle scalability and efficiency challenges, we introduce a graph-aware token reduction module. Extensive experiments across various models and datasets show that our method achieves state-of-the-art performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues. Codes are available at https://github.com/wykk00/GraphBridge.",
}
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<abstract>Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information. Research in this field generally consist of two main perspectives: local-level encoding and global-level aggregating, respectively refer to textual node information unification (e.g., using Language Models) and structure-augmented modeling (e.g., using Graph Neural Networks). Most existing works focus on combining different information levels but overlook the interconnections, i.e., the contextual textual information among nodes, which provides semantic insights to bridge local and global levels. In this paper, we propose GraphBridge, a multi-granularity integration framework that bridges local and global perspectives by leveraging contextual textual information, enhancing fine-grained understanding of TAGs. Besides, to tackle scalability and efficiency challenges, we introduce a graph-aware token reduction module. Extensive experiments across various models and datasets show that our method achieves state-of-the-art performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues. Codes are available at https://github.com/wykk00/GraphBridge.</abstract>
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%0 Conference Proceedings
%T Bridging Local Details and Global Context in Text-Attributed Graphs
%A Wang, Yaoke
%A Zhu, Yun
%A Zhang, Wenqiao
%A Zhuang, Yueting
%A Liyunfei, Liyunfei
%A Tang, Siliang
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-bridging-local
%X Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information. Research in this field generally consist of two main perspectives: local-level encoding and global-level aggregating, respectively refer to textual node information unification (e.g., using Language Models) and structure-augmented modeling (e.g., using Graph Neural Networks). Most existing works focus on combining different information levels but overlook the interconnections, i.e., the contextual textual information among nodes, which provides semantic insights to bridge local and global levels. In this paper, we propose GraphBridge, a multi-granularity integration framework that bridges local and global perspectives by leveraging contextual textual information, enhancing fine-grained understanding of TAGs. Besides, to tackle scalability and efficiency challenges, we introduce a graph-aware token reduction module. Extensive experiments across various models and datasets show that our method achieves state-of-the-art performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues. Codes are available at https://github.com/wykk00/GraphBridge.
%U https://aclanthology.org/2024.emnlp-main.823
%P 14830-14841
Markdown (Informal)
[Bridging Local Details and Global Context in Text-Attributed Graphs](https://aclanthology.org/2024.emnlp-main.823) (Wang et al., EMNLP 2024)
ACL
- Yaoke Wang, Yun Zhu, Wenqiao Zhang, Yueting Zhuang, Liyunfei Liyunfei, and Siliang Tang. 2024. Bridging Local Details and Global Context in Text-Attributed Graphs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 14830–14841, Miami, Florida, USA. Association for Computational Linguistics.