Paper Citation Record · LEDGER

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models

As of 15 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2508.20583.

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

pith.paper-citation-record.v1
2508.20583 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

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measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T23:37:56.987730Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02e91ae1-ea28-4716-ab2a-25eae3614bd1 · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Understanding intermediate layers using linear classifier probes

Reference 1

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Observation 499da54e-241e-424f-b495-a9eacf6d95a7 · outbound

This paper cites Semma: A semantic aware knowledge graph foundation model,.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Semma: A semantic aware knowledge graph foundation model,

Reference 2

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Observation ebbdbb0d-441e-4f57-ab4e-9c0aa45dda5d · outbound

This paper cites LLaGA: Large Language and Graph Assistant.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models LLaGA: Large Language and Graph Assistant

Reference 3

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Observation 5ecc2fe0-22b8-425c-b7f4-381036840e69 · outbound

This paper cites Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction

Reference 4

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Observation e739a498-3c48-45c5-b2d4-cb5fe104dcdb · outbound

This paper cites Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023

Reference 5

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Observation eac39020-a5d6-483d-978f-18e40bb7bba7 · outbound

This paper cites Inductive Representation Learning on Large Graphs.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Inductive Representation Learning on Large Graphs

Reference 6

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Observation 8b5d199f-0a8d-4995-81ec-e8518e016621 · outbound

This paper cites Representation Learning on Graphs: Methods and Applications.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Representation Learning on Graphs: Methods and Applications

Reference 7

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Observation 5ff26438-b4b4-4935-a568-6fcc6f80ed02 · outbound

This paper cites Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

Reference 8

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Observation b8c7cafe-d1d9-4d5b-a1a1-930edf8f507b · outbound

This paper cites G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

Reference 9

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Observation 47697848-8286-49ac-b137-bb653256ed96 · outbound

This paper cites Linkgpt: Teaching large language models to predict missing links, 2024.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Linkgpt: Teaching large language models to predict missing links, 2024

Reference 10

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Observation 8aa5dd65-8cec-4f69-918e-5f9b6d1f704b · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs,.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Open graph benchmark: Datasets for machine learning on graphs,

Reference 11

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Observation a056b6ab-0c29-429a-8ca6-cfd6bb8bb64d · outbound

This paper cites Patton: Language Model Pretraining on Text-Rich Networks.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Patton: Language Model Pretraining on Text-Rich Networks

Reference 12

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Observation 3cb3adc1-34aa-4fa3-a1af-c7fc4b2819a8 · outbound

This paper cites Large Language Models on Graphs: A Comprehensive Survey.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Large Language Models on Graphs: A Comprehensive Survey

Reference 13

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Observation a5812ff3-91e6-4ecb-b5d2-136f18dbe43b · outbound

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

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Semi-Supervised Classification with Graph Convolutional Networks

Reference 14

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Observation 3b6f01de-de51-4ff5-9e60-e56824eda071 · outbound

This paper cites Similarity of Neural Network Representations Revisited.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Similarity of Neural Network Representations Revisited

Reference 15

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Observation afdeec44-7dc6-4b1b-b1c2-0fb616553fa2 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 16

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Observation 19814c35-887c-4529-a92e-b5165319a89f · outbound

This paper cites A Survey of Graph Meets Large Language Model: Progress and Future Directions.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models A Survey of Graph Meets Large Language Model: Progress and Future Directions

Reference 17

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Observation 72a9ab7a-2176-4cdc-92b7-13f5850a21a5 · outbound

This paper cites Glbench: A comprehensive benchmark for graph with large language models,.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Glbench: A comprehensive benchmark for graph with large language models,

Reference 18

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Observation 31ec2259-c4e1-4d47-ae57-7758cccf178f · outbound

This paper cites One for All: Towards Training One Graph Model for All Classification Tasks.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models One for All: Towards Training One Graph Model for All Classification Tasks

Reference 19

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Observation 46e9f151-20b4-48d6-9551-37635a606aa8 · outbound

This paper cites Visual Instruction Tuning.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Visual Instruction Tuning

Reference 20

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Observation 7252fc00-2869-4e10-a739-cbe509c7c7e4 · outbound

This paper cites Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 21

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This paper cites Mack and A.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Mack and A

Reference 22

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Observation 76b13fe1-0a1b-4c07-9659-f145abd5de77 · outbound

This paper cites Automating the construction of internet portals with machine learning.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Automating the construction of internet portals with machine learning

Reference 23

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Observation 6f4ab305-16ad-4af1-95ca-b119117a85bc · outbound

This paper cites Let Your Graph Do the Talking: Encoding Structured Data for LLMs.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Let Your Graph Do the Talking: Encoding Structured Data for LLMs

Reference 24

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This paper cites Pitfalls of graph neural network evaluation, 2019.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Pitfalls of graph neural network evaluation, 2019

Reference 25

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Observation 3ed5e52e-4cca-4d47-9ee0-99e7e34c067c · outbound

This paper cites Graph Attention Networks.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Graph Attention Networks

Reference 26

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Observation e446754b-9131-419f-bf7b-660ce3a1befa · outbound

This paper cites LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings

Reference 27

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Observation fbec7b48-3622-43c4-a2a9-dcd256631e64 · outbound

This paper cites GraphFM: A Comprehensive Benchmark for Graph Foundation Model.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models GraphFM: A Comprehensive Benchmark for Graph Foundation Model

Reference 28

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Observation 43dada6f-6f68-470e-81ab-64f78d7334a0 · outbound

This paper cites A comprehensive study on text-attributed graphs: Benchmarking and rethink- ing.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models A comprehensive study on text-attributed graphs: Benchmarking and rethink- ing

Reference 29

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Observation 888efd08-5a91-49f2-bebb-b976ef64eed7 · outbound

This paper cites GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph

Reference 30

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This paper cites The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision).

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)

Reference 31

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Revisiting Semi-Supervised Learning with Graph Embeddings

Reference 32

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This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 33

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This paper cites QA- GNN: Reasoning with language models and knowledge graphs for question answering.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models QA- GNN: Reasoning with language models and knowledge graphs for question answering

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models GNNExplainer: Generating Explanations for Graph Neural Networks

Reference 35

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This paper cites Learning on Large-scale Text-attributed Graphs via Variational Inference.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Learning on Large-scale Text-attributed Graphs via Variational Inference

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models chat with their graph

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Blue Line

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This paper cites A specified number of initial station locations are then calculated by evaluating points along this curve.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models A specified number of initial station locations are then calculated by evaluating points along this curve

Reference 42

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models A KD-Tree is used to efficiently find all stations across all lines that are within a minimum distance threshold of each other

Reference 43

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models If a sequence of stations on a line was A → B → C and stations B and C were coalesced into a new station D, the resulting edges would connect A → D

Reference 44

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models connector

Reference 45

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models This prevents models from learning spurious correlations from the names themselves and forces them to rely solely on the graph’s topology and categorical features

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:44.422414Z

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models These are organized by group (e.g., lookup, comparison) and type (e.g., existence, counting)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:44.410376Z

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models In each iteration, it randomly selects a QuestionForm and attempts to instantiate it using the current graph

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:44.398171Z

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This paper cites How many stations on the Red Line are large?.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models How many stations on the Red Line are large?

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:44.384974Z

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Observation 3d0c489c-d53e-4e13-8d4d-439301b45daf · outbound

This paper cites Line-Centric): The process begins by generating a target number of nodes, as specified by the nodes parameter.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Line-Centric): The process begins by generating a target number of nodes, as specified by the nodes parameter

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:44.371762Z

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Pre-Defined Paths): Edge creation is not de- termined by a sequential path

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:44.358162Z

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Interchange Creation): The function coalesce_nearby_nodes serves a different conceptual purpose here

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:44.344136Z

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A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models yes”, “no

Reference 53

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Observation fc7841b2-f986-42a8-95b1-4837cde75461 · outbound

This paper cites Open Graph Benchmark: Datasets for Machine Learning on Graphs.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 2021

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Source-reported events for the cited work

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Observation 22bb43ec-3221-4c02-af50-fd4891ce600f · outbound

This paper cites GLBench: A Comprehensive Benchmark for Graph with Large Language Models.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models GLBench: A Comprehensive Benchmark for Graph with Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T16:47:43.634392Z

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Observation f12b70a0-9c1c-4dd2-937d-fddf33e8e32a · outbound

This paper cites an unresolved cited work.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Unresolved cited work

Reference 2025

Resolution: verified exact; raw_fallback, observed 2026-08-15T16:47:44.302345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation a119f3bf-caf9-4d5a-b73d-0f01af0b564c · inbound

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models cites this paper.

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T23:37:56.987730Z

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