Paper Citation Record · LEDGER

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.08168.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.08168 v1

Coverage vector

measured 40 of 40 reference resolution

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measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation ab77a6e5-3821-4ca6-a0bb-3d0695941ce0 · outbound

This paper cites Label-free Node Classification on Graphs with Large Language Models (LLMS).

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Label-free Node Classification on Graphs with Large Language Models (LLMS)

Reference 1

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This paper cites Graph neural networks for recommender system.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Graph neural networks for recommender system

Reference 7

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Observation 8e2c1ef3-28f2-4b57-9422-7382a5ef97db · outbound

This paper cites Momentum contrast for un- supervised visual representation learning.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Momentum contrast for un- supervised visual representation learning

Reference 10

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This paper cites Gpt-gnn: Genera- tive pre-training of graph neural networks.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Gpt-gnn: Genera- tive pre-training of graph neural networks

Reference 11

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Observation a0b962f1-e8f6-4328-be79-90f02ab07e0a · outbound

This paper cites Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs

Reference 12

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Observation a0e3cacc-18ce-4635-866f-18f26d9c0e76 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Semi-Supervised Classification with Graph Convolutional Networks

Reference 13

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Observation fa1b55b7-c187-41bb-b6ac-c96b061c8da5 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 16

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Observation 4471f9b1-75d7-4093-881d-d9e989c3ed0a · outbound

This paper cites Few-shot node classification on attributed networks with graph meta-learning.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Few-shot node classification on attributed networks with graph meta-learning

Reference 18

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This paper cites Graphprompt: Unifying pre-training and downstream tasks for graph neural networks.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Graphprompt: Unifying pre-training and downstream tasks for graph neural networks

Reference 19

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This paper cites Automat- ing the construction of internet portals with machine learn- ing.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Automat- ing the construction of internet portals with machine learn- ing

Reference 20

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This paper cites Graphgpt: Graph instruction tuning for large language models.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Graphgpt: Graph instruction tuning for large language models

Reference 23

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This paper cites Attention is all you need.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Attention is all you need

Reference 24

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This paper cites Graph Attention Networks.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Graph Attention Networks

Reference 25

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This paper cites Deep Graph Infomax.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Deep Graph Infomax

Reference 26

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This paper cites Generative and contrastive paradigms are complementary for graph self-supervised learning.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Generative and contrastive paradigms are complementary for graph self-supervised learning

Reference 27

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This paper cites TESA: A trajectory and semantic-aware dy- namic heterogeneous graph neural network.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph TESA: A trajectory and semantic-aware dy- namic heterogeneous graph neural network

Reference 28

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Observation d3b98b45-8f00-4ed4-840e-3232d627a32b · outbound

This paper cites Aug- menting low-resource text classification with graph- grounded pre-training and prompting.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Aug- menting low-resource text classification with graph- grounded pre-training and prompting

Reference 29

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This paper cites Infogcl: Information-aware graph contrastive learning.Advances in Neural Information Processing Systems, 34:30414–30425,.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Infogcl: Information-aware graph contrastive learning.Advances in Neural Information Processing Systems, 34:30414–30425,

Reference 30

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This paper cites A comprehensive study on text-attributed graphs: Benchmarking and re- thinking.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph A comprehensive study on text-attributed graphs: Benchmarking and re- thinking

Reference 31

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This paper cites Oceanbase: a 707 million tpmc dis- tributed relational database system.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Oceanbase: a 707 million tpmc dis- tributed relational database system

Reference 32

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This paper cites Oceanbase paetica: A hybrid shared-nothing/shared- everything database for supporting single machine and dis- tributed cluster.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Oceanbase paetica: A hybrid shared-nothing/shared- everything database for supporting single machine and dis- tributed cluster

Reference 33

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Observation 22b90f48-7e25-4c6e-97d6-eac5c94903b4 · outbound

This paper cites Graph convolutional networks for text classification.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Graph convolutional networks for text classification

Reference 34

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Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Graph con- trastive learning with augmentations

Reference 35

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This paper cites Enhancing social recom- mendation with adversarial graph convolutional networks.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Enhancing social recom- mendation with adversarial graph convolutional networks

Reference 36

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This paper cites Graphtranslator: Align- ing graph model to large language model for open-ended tasks.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Graphtranslator: Align- ing graph model to large language model for open-ended tasks

Reference 37

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This paper cites Tgraph: A tensor-centric graph processing framework.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Tgraph: A tensor-centric graph processing framework

Reference 38

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This paper cites Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs

Reference 39

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Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Conditional prompt learning for vision-language models

Reference 40

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Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Graph-based anomaly detection

Reference 2000

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Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Gppt: Graph pre-training and prompt tuning to generalize graph neural networks

Reference 2003

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This paper cites Towards scalable and deep graph neural networks via noise masking.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Towards scalable and deep graph neural networks via noise masking

Reference 2016

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This paper cites Palf: Replicated write-ahead logging for distributed databases.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Palf: Replicated write-ahead logging for distributed databases

Reference 2017

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Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Universal prompt tuning for graph neural networks

Reference 2018

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This paper cites Relative and absolute location embed- ding for few-shot node classification on graph.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Relative and absolute location embed- ding for few-shot node classification on graph

Reference 2019

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Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Graph neural network-based anomaly detection in multivariate time series

Reference 2020

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2021

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Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Inductive representation learning on large graphs

Reference 2022

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This paper cites Exploring the potential of large language models (llms) in learning on graphs.

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Exploring the potential of large language models (llms) in learning on graphs

Reference 2023

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Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Debiased contrastive learning

Reference 2024

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Observation e5fdc118-8e16-4951-9ae4-677dfe54b9d0 · outbound

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Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph Prototypical graph contrastive learning

Reference 2025

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