GRAPH NEURAL NETWORKS

How to Make LMs Strong Node Classifiers? (2026.findings-eacl)

Challenge:	Named Entity Recognition (NER) uses sequence labelling and span classification to identify entities.
Approach:	They propose a framework that uses Graph Neural Networks to enrich the span representation to reduce the number of overlapping spans during prediction.
Outcome:	The proposed framework reduces the number of overlapping spans while maintaining competitive metric performance.

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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong

Information Retrieval

Challenge:	Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs).
Approach:	They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications.
Outcome:	The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication.

GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution (2022.coling-1)

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Danfeng Guo, Arpit Gupta, Sanchit Agarwal, Jiun-Yu Kao, Shuyang Gao, Arijit Biswas, Chien-Wei Lin, Tagyoung Chung, Mohit Bansal

Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)

Challenge:	Multimodal coreference resolution (MCR) is a crucial capability for building next-generation conversational agents.
Approach:	They propose a multimodal coreference resolution model that resolves coreferences made in multi-turn dialogues with scene images.
Outcome:	The proposed model resolves coreferences made in multi-turn dialogues with scene images.

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Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, Liang Wang

GAINER: Graph Machine Learning with Node-specific Radius for Classification of Short Texts and Documents (2024.eacl-long)

Challenge:	Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words.
Approach:	They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings.
Outcome:	The proposed method outperforms state-of-the-art methods on four benchmark datasets.

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Naganand Yadati

Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters (2023.eacl-main)

Challenge:	Recent advances in Graph Machine Learning (GML) have led to the development of numerous models tailored for processing text for various natural language applications.
Approach:	They propose a framework called Graph mAchine learnIng with Node-spEcific Radius that is aimed at graph-based NLP.
Outcome:	The proposed framework is non-neural and novel for graph-based NLP.

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Sebastien Montella, Alexis Nasr, Johannes Heinecke, Frederic Bechet, Lina M. Rojas Barahona

GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism (2024.findings-naacl)

Challenge:	Recent approaches to text generation from Abstract Meaning Representation (AMR) have been based on neural-centered encoderdecoder architectures.
Approach:	They propose a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks.
Outcome:	The proposed adapter is robust to a variety of approaches and can be used to generate Graph-to-Text representations.

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Shuzhou Yuan, Michael Färber

LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification (2025.findings-naacl)

Challenge:	Pretrained Language Models (PLMs) benefit from external knowledge stored in graph structures for various downstream tasks.
Approach:	They propose a graph-guided self-attention mechanism that integrates token-level structural information into PLMs without additional alignment or concatenation efforts.
Outcome:	The proposed model outperforms baseline models and achieves comparable results to state-of-the-art models on WebNLG datasets.

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Shubham Kumar Nigam, Tanmay Dubey, Govind Sharma, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya

Text Classification

Challenge:	a lack of large-scale annotated datasets hinders effective training of ML models . despite advances in semantic segmentation, challenges persist in distinguishing between closely related roles .
Approach:	They propose a large annotated dataset for semantic segmentation of legal documents . they use a rhetorical role classification model to compare performance against other models .
Outcome:	The largest annotated dataset for this task outperforms models relying on sentence-level features.

Question-Answer Sentence Graph for Joint Modeling Answer Selection (2023.eacl-main)

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Roshni Iyer, Thuy Vu, Alessandro Moschitti, Yizhou Sun

Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models (2025.findings-acl)

Challenge:	Existing approaches to automate Question Answering (QA) are graph-based and can target large text databases.
Approach:	They propose graph-based approaches for Answer Sentence Selection (AS2) . they train and integrate state-of-the-art (SOTA) models for computing scores .
Outcome:	The proposed approach outperforms baseline models on academic benchmarks and a real-world dataset on unseen queries.

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Haochen Liu, Song Wang, Chen Chen, Jundong Li

Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition (2021.acl-short)

Challenge:	Existing methods for large language models require costly fine-tuning or retrieve noisy KG information.
Approach:	They propose to generate KG-based input embedding prefixes as soft prompts but fail to account for question relevance, resulting in noisy prompts.
Outcome:	The proposed model outperforms state-of-the-art methods across multiple datasets.

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Pei Chen, Haibo Ding, Jun Araki, Ruihong Huang

Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction (2021.findings-acl)

Challenge:	Named entity recognition (NER) is well studied for the general domain, but the performance is still moderate for specialized domains.
Approach:	They propose to explicitly connect entity mentions based on global coreference relations and local dependency relations to build better entity mention representations.
Outcome:	The proposed system improves the NER performance even with a tiny amount of labeled data.

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Zhexue Chen, Hong Huang, Bang Liu, Xuanhua Shi, Hai Jin

Sentiment Analysis

Challenge:	Existing approaches to extract triplets from sentences neglect the mutual information between aspects and have the problem of error propagation.
Approach:	They propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model to exploit the syntactical and semantic relationships between the triplet elements and jointly extract them.
Outcome:	The proposed model outperforms existing methods on four benchmark datasets and significantly outperformed existing approaches.

OpenGraph: Towards Open Graph Foundation Models (2024.findings-emnlp)

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Lianghao Xia, Ben Kao, Chao Huang

Document-level Relation Extraction with Dual-tier Heterogeneous Graph (2020.coling-main)

Challenge:	Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information and improving performance in tasks like link prediction and node classification.
Approach:	They propose a graph foundation model that generalizes to unseen graph data with different properties.
Outcome:	The proposed model achieves remarkable zero-shot graph learning performance across various settings.

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Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, Li Guo

LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs (2024.findings-acl)

Challenge:	Existing methods focus on extracting relations from single sentence . document-level relation extraction requires a comprehension of the whole document .
Approach:	They propose a graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level relation extraction.
Outcome:	The proposed model achieves state-of-the-art performance on two widely used datasets.

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Kai Wang, Yuwei Xu, Zhiyong Wu, Siqiang Luo

Graph Language Models (2024.acl-long)

Challenge:	Knowledge Graph (KG) inductive reasoning is widely adopted in various applications.
Approach:	They propose a framework for low-resource inductive reasoning using Large Language Models to generate a graph-structural prompt for pre-trained KGs.
Outcome:	The proposed framework outperforms previous methods in three-shot, one-shot and zero-shot reasoning tasks.

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Moritz Plenz, Anette Frank

Bridging Attribution and Open-Set Detection using Graph-Augmented Instance Learning in Synthetic Speech (2026.eacl-long)

Challenge:	Language Models (LMs) are the workhorses of NLP, but their interplay with structured knowledge graphs (KGs) is still actively researched.
Approach:	They propose a Graph Language Model (GLM) that integrates the strengths of both approaches and mitigates their weaknesses.
Outcome:	Empirical evaluations show that the proposed model surpasses both LM- and GNN-based baselines in supervised and zero-shot setting, demonstrating their versatility.

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Mohd Mujtaba Akhtar, null Girish, Farhan Sheth, Muskaan Singh

Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs (P19-1)

Challenge:	Synthetic speech detection is a critical part of safeguarding digital communication, enabling systems to identify and mitigate the risks posed by highly realistic, machine-generated voices.
Approach:	They propose a framework that combines SFMs with graph-based modeling and open-set generalization to capture meaningful relationships between utterances and recognize speech that doesn’t belong to any known generator.
Outcome:	The proposed framework improves performance across both tasks, with Mamba-based embeddings delivering particularly strong results.

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Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou

Reasoning

Challenge:	Existing models to tackle multi-hop reading comprehension (RC) are focusing on a single document or paragraph, but they lack the ability to do reasoning across multiple documents.
Approach:	They propose a heterogeneous document-entity graph with different types of nodes and edges to solve multi-hop RC problem.
Outcome:	The proposed model can do reasoning over the proposed graph with nodes representation initialized with co-attention and self-attention based context encoders.

Judge and Improve: Towards a Better Reasoning of Knowledge Graphs with Large Language Models (2025.emnlp-main)

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Mo Zhiqiang, Yang Hua, Jiahui Li, Yuan Liu, Shawn Wong, Jianmin Huang

Neural Topic Modeling by Incorporating Document Relationship Graph (2020.emnlp-main)

Challenge:	Existing approaches to integrating graph and language models face two key limitations: achieving robust semantic alignment and ensuring interpretability in outputs.
Approach:	They propose a framework to integrate graph and language modalities while enhancing transparency.
Outcome:	Extensive experiments on three benchmark datasets show that the proposed framework surpasses existing methods in efficiency and generates outputs that are significantly more interpretable.

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Deyu Zhou, Xuemeng Hu, Rui Wang

A Knowledge Graph Reasoning-Based Model for Computerized Adaptive Testing (2025.coling-main)

Challenge:	Graph Topic Models (GNNs) capture relationships between graph nodes via message passing . recent research has focused on topic modeling using latent Dirichlet Allocation .
Approach:	They propose a Graph Topic Model (GTM) that captures relationships between graph nodes via message passing.
Outcome:	The proposed model captures the relationships between nodes via message passing . the results demonstrate that the proposed model is effective in generating documents .

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Xinyi Qiu, Zhiyun Chen

ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction (2023.findings-emnlp)

Challenge:	Existing studies have failed to account for the differences in concept relevance when a question involves multiple concepts .
Approach:	They propose a Knowledge Graph Reasoning-Based Model for CAT that captures semantic and relational information between concepts and questions and incorporates multiple evaluation objectives.
Outcome:	The proposed model outperforms existing methods on three authentic educational datasets.

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Yaorui Shi, An Zhang, Enzhi Zhang, Zhiyuan Liu, Xiang Wang

JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering (2022.naacl-main)

Challenge:	Existing methods for predicting chemical reactions are limited by insufficient training data and inability to utilize textual information.
Approach:	They propose a framework that leverages chemical knowledge encoded in language models to assist GNNs, thereby enhancing the accuracy of real-world chemical reaction predictions.
Outcome:	The proposed framework improves state-of-the-art GNN-based methods across chemical reaction datasets especially in out-of distribution settings.

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Yueqing Sun, Qi Shi, Le Qi, Yu Zhang

MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media (2025.naacl-long)

Challenge:	Existing KG-augmented models for commonsense question answering ignore the effectively fusing and reasoning over question context representations and the KG representations.
Approach:	They propose a novel model which combines a logical reasoning and a dynamic pruning mechanism to solve these limitations.
Outcome:	The proposed model improves existing models and performs interpretable reasoning on the CommonsenseQA and OpenBookQA datasets.

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Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov, Shangsong Liang

Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs (2025.coling-main)

Challenge:	Existing methods for profiling news media focus on textual features, causing them to overlook complex relationships between entities.
Approach:	They propose a framework for profiling news media from the lens of political bias and factuality.
Outcome:	The proposed framework improves existing models and improves them by integrating structural information from similar nodes.

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Taiqiang Wu, Zhe Zhao, Jiahao Wang, Xingyu Bai, Lei Wang, Ngai Wong, Yujiu Yang

Green & Sustainable NLP

Challenge:	Existing methods to train low-latency multilayer perceptrons (MLPs) on graph tasks are based on graph nodes and lack graph structural information.
Approach:	They propose to distill graph structural information from Graph Neural Networks (GNNs) to low-latency multilayer perceptrons (MLPs) on graph tasks.
Outcome:	The proposed method does not require graph edges (edge-free setting) yet learns structure-aware MLPs.

Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective on Molecule Graphs (2024.findings-emnlp)

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Yinhan He, Zaiyi Zheng, Patrick Soga, Yaochen Zhu, Yushun Dong, Jundong Li

Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation (2022.coling-1)

Challenge:	Graph Neural Networks (GNNs) are successful in molecular property prediction tasks, but their outputs are often black-box and not easily understandable by humans.
Approach:	They propose a method to unleash the power of large language models (LLMs) to explain GNNs for molecular property prediction.
Outcome:	The proposed method uses autoencoder to generate the counterfactual graph topology from a set of counterfact text pairs based on an input graph.

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Binh Nguyen, Long Nguyen, Dien Dinh

Automated Molecular Concept Generation and Labeling with Large Language Models (2025.coling-main)

Challenge:	Recent studies have used Graph Neural Networks (GNNs) to encode language knowledge into token embeddings.
Approach:	They propose a multi-level community-awareness Graph Neural Network layer to jointly model local and global relationships between words and their linguistic roles in multiple communities.
Outcome:	The proposed method reduces time complexity in very long sentences while preserving the original meaning.

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Zimin Zhang, Qianli Wu, Botao Xia, Fang Sun, Ziniu Hu, Yizhou Sun, Shichang Zhang

MuSe: Multi-Stage Graph Reasoning via Vision-Language Models (2026.acl-long)

Challenge:	Concept-based models lack explainability and need predefined concepts and manual labeling in molecular science.
Approach:	They propose a framework that leverages Large Language Models to generate and label predictive molecular concepts without human input.
Outcome:	The proposed framework outperforms existing models on several benchmarks while maintaining explainability and allowing easy intervention.

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Guanyu Wang, Xu Chu, Zhijie Tan, Xinrong Chen, Tong Mo, Weiping Li

Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction (2024.lrec-main)

Challenge:	Graph Neural Networks (GNNs) and graph transformers are inadequate for tasks with limited generalization.
Approach:	They propose a multi-stage graph reasoning framework based on vision-language models that incrementally samples and visualizes task-relevant subgraphs.
Outcome:	The proposed framework outperforms existing benchmarks in Graph-related tasks.

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Xiaowei Zhao, Yong Zhou, Xiujuan Xu

Sentiment Analysis

Challenge:	Aspect Sentiment Triple Extraction (ASTE) is an advanced natural language processing task.
Approach:	They propose a Dual Encoder: Exploiting the potential of Syntactic and Semantic model which maximizes syntactical and semantic relationships among words.
Outcome:	The proposed model surpasses the current state-of-the-art on public benchmarks and shows that it is highly efficient.

TabPrompt: Graph-based Pre-training and Prompting for Few-shot Table Understanding (2023.findings-emnlp)

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Rihui Jin, Jianan Wang, Wei Tan, Yongrui Chen, Guilin Qi, Wang Hao

Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)

Challenge:	Existing methods of Table Understanding (TU) focus on the textual content within the tabular data, disregarding the topological information of the table.
Approach:	They propose a framework that uses tabs to understand tabular data without ignoring the topological information of the table.
Outcome:	The proposed framework outperforms baselines in few-shot table understanding tasks.

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Margarita Bugueño, Gerard de Melo

Parsing into Variable-in-situ Logico-Semantic Graphs (2020.acl-main)

Challenge:	Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics.
Approach:	They compare graph representation methods for text classification using different architectures and setups.
Outcome:	The proposed graph representation methods outperform other models in document comprehension tasks.

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Yufei Chen, Weiwei Sun

Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages (2025.coling-main)

Challenge:	a new type of graph-based meaning representation allows analysis for scope-related phenomena.
Approach:	They propose variable-in-situ logico-semantic graphs to bridge gap between semantic graph and logical form parsing.
Outcome:	The proposed graph-based meaning representation achieves 92.39% accuracy in terms of elementary dependency match . the output of the proposed parser is highly coherent .

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Sello Ralethe, Jan Buys

Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs (2021.emnlp-main)

Challenge:	Knowledge bases (KBs) in low-resource languages are often incomplete, restricting the ability to do zero-shot question answering using multilingual language models.
Approach:	They propose a novel cross-lingual mapping technique which improves word alignments extracted from parallel English-LRL text by combining lexical alignment, named entity recognition, and semantic alignment.
Outcome:	The proposed approach improves zero-shot question answering accuracy by up to 17% compared to baselines without KB access.

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Chengjin Xu, Fenglong Su, Jens Lehmann

GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)

Challenge:	Existing embedding-based approaches disregard time information that exists in many large-scale knowledge graphs, leaving much room for improvement.
Approach:	They propose a Time-aware Entity Alignment approach that incorporates relation and time information into a vector space and uses Graph Neural Networks to learn entity representations.
Outcome:	The proposed approach outperforms the state-of-the-art methods on real-world TKG datasets due to the inclusion of time information.

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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li

Digest the Knowledge: Large Language Models empowered Message Passing for Knowledge Graph Question Answering (2025.acl-long)

Challenge:	Existing fact-checking methods that use large language models often generate subtle factual errors.
Approach:	They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation.
Outcome:	GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call.

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Junhong Wan, Tao Yu, Kunyu Jiang, Yao Fu, Weihao Jiang, Jiang Zhu

RecLM: Recommendation Instruction Tuning (2025.acl-long)

Challenge:	Existing methods to augment large language models (LLMs) with external knowledge are unorganized and unorganized.
Approach:	They propose a method that learns a concise facts graph and encodes it into multi-level lists of texts to augment LLMs.
Outcome:	The proposed method improves on all 5 knowledge graph question answering datasets and offers human-level semantic explainability.

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Yangqin Jiang, Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang

Fair Text-Attributed Graph Representation Learning (2025.findings-emnlp)

Challenge:	Modern recommender systems aim to understand user-item relationships through past interactions, but their effectiveness is limited when handling sparse data or zero-shot scenarios.
Approach:	They propose a model-agnostic recommendation instruction-tuning paradigm that integrates large language models with collaborative filtering.
Outcome:	The proposed model-agnostic recommendation instruction-tuning paradigm improves performance across various settings and plug-and-play compatibility with state-of-the-art recommender systems.

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Ruilin Luo, Tianle Gu, Lin Wang, Yunfeng Zhou, Songtao Jiang, Lei Wang, Yujiu Yang

Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis (2024.lrec-main)

Challenge:	Text-Attributed Graphs (TAGs) inherit issues from Graph Neural Networks such as fairness.
Approach:	They propose to evolve LM-as-encoder to LM as-fair-encoding process to explore fairness in TAGRL.
Outcome:	The proposed process can be integrated with fairness-enhancing strategies on the GNNs decoder side.

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Akash Anil, Victor Gutierrez-Basulto, Yazmin Ibanez-Garcia, Steven Schockaert

Bridging Local Details and Global Context in Text-Attributed Graphs (2024.emnlp-main)

Challenge:	Existing methods for inductive knowledge graph completion are underperforming . implausible entities are not ranked and only the most informative path is taken into account .
Approach:	They propose to use a rule-based approach to find plausible triples missing from a given KG.
Outcome:	The proposed models outperform state-of-the-art methods on inductive knowledge graph completion.

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Yaoke Wang, Yun Zhu, Wenqiao Zhang, Yueting Zhuang, Liyunfei Liyunfei, Siliang Tang

Green & Sustainable NLP

Challenge:	Existing studies focus on combining different information levels but overlook interconnections, i.e., contextual textual information among nodes.
Approach:	They propose a framework that bridges local and global perspectives by leveraging contextual textual information.
Outcome:	The proposed framework achieves state-of-the-art performance while reducing tokens significantly.

Can Large Language Models Enhance Predictions of Disease Progression? Investigating Through Disease Network Link Prediction (2024.emnlp-main)

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Haohui Lu, Usman Naseem

LGA: LLM-GNN Aggregation for Temporal Evolution Attribute Graph Prediction (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have made significant strides in various tasks, yet their effectiveness in predicting disease progression remains relatively unexplored.
Approach:	They propose a large language model with graph prompting and Retrieval-augmented generation to enhance the prediction performance of disease comorbidity within disease networks.
Outcome:	The proposed model outperforms Graph Neural Networks and Graph Prompts and Retrieval-Augmented Generation models in disease progression prediction tasks.

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Feng Zhao, Ruoyu Chai, Kangzheng Liu, Xianggan Liu

Representation Learning

Challenge:	Current methods focus on 1-hop neighborhood aggregation, lacking capability to capture complex structural interactions.
Approach:	They propose a framework that integrates structural information into attribute embeddings through an attribute embedded loss.
Outcome:	The proposed framework shows significant improvements over existing methods on real-world datasets.

Graph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning (2025.emnlp-main)

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Yicong Wu, Guangyue Lu, Yuan Zuo, Huarong Zhang, Junjie Wu

Reasoning

Challenge:	Recent advances in Large Reasoning Models (LLMs) provide a zero-shot alternative via explicit, long chain-of-thought reasoning.
Approach:	They propose a GNN-free approach that reformulates graph tasks as textual reasoning problems solved by LRMs.
Outcome:	The proposed approach outperforms state-of-the-art baselines in zero-shot settings, producing interpretable and effective predictions.

Can Large Language Models Act as Ensembler for Multi-GNNs? (2025.emnlp-main)

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Hanqi Duan, Yao Cheng, Jianxiang Yu, Yao Liu, Xiang Li

A Graph Talks, But Who’s Listening? Rethinking Evaluations for Graph-Language Models (2026.findings-acl)

Challenge:	Existing graph neural networks lack the inherent semantic understanding capability of rich textual attributes, limiting their effectiveness in applications.
Approach:	They propose a model that integrates multiple GNNs and LLMs to provide an ensemble for multi-GNNs.
Outcome:	The proposed model outperforms existing models in terms of semantic understanding of graph structures and graph structures.

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Soham Petkar, Hari Aakash K, Anirudh Vempati, Akshit Sinha, Ponnurangam Kumaraguru, Chirag Agarwal