Papers with node classification
Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph (2023.eacl-demo)
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| Challenge: | Graphs are a natural representation of complex data as their structure allows users to discover (often implicit) relations among the nodes intuitively. |
| Approach: | They propose a corpus annotation graph framework that extends graphs with automatically extracted annotations. |
| Outcome: | The proposed framework can be used for further analyses across multiple downstream tasks. |
Graph Representation Learning in Hyperbolic Space via Dual-Masked (2025.coling-main)
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| Challenge: | Existing MR-based methods do not fully consider deep node and structural information. |
| Approach: | They propose a graph dual-masked self-supervised graph representation learning framework in hyperbolic space that masks nodes and edges and performs node aggregation. |
| Outcome: | The proposed method is superior in downstream tasks such as node classification and link prediction. |
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)
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Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding
| Challenge: | Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges. |
| Approach: | They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs. |
| Outcome: | The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges . |
Mitigating Degree Bias in Hypergraphs via Attribute-as-Structure Approach (2026.eacl-long)
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| Challenge: | Existing “attribute-as-feature” approaches do not create new aggregation pathways for sparsely connected entities. |
| Approach: | They propose an “attribute-as-structure” approach specifically designed for heterogeneous hypergraphs that integrates attributes directly into the hypergraph topology as distinct node types. |
| Outcome: | The proposed approach integrates attributes directly into the hypergraph topology as distinct node types, creating new structural pathways to enrich sparsely connected entities while preserving semantic distinctiveness within complex complex interactions. |
OpenGraph: Towards Open Graph Foundation Models (2024.findings-emnlp)
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| 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. |
Edge: Enriching Knowledge Graph Embeddings with External Text (2021.naacl-main)
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| Challenge: | Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. |
| Approach: | They propose a knowledge graph enrichment framework called Edge to enhance knowledge graphs based on "hard" co-occurrence of words in knowledge graph entities and external text. |
| Outcome: | The proposed framework achieves "soft" augmentation by combining external text with knowledge graph entities. |
Exploring the Potential of Large Language Models for Heterophilic Graphs (2025.naacl-long)
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| Challenge: | Existing approaches for heterophilic graphs overlook rich textual data associated with nodes, which could unlock deeper insights into their heterophilistic contexts. |
| Approach: | They propose a two-stage framework to enhance node classification on heterophilic graphs by leveraging open-world knowledge encoded by large language models. |
| Outcome: | The proposed framework can be used to better characterize heterophilic graphs, where neighboring nodes often exhibit different labels. |
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks (2022.emnlp-main)
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| Challenge: | Existing meta-path generation methods cannot fully exploit rich textual information in HINs. |
| Approach: | They propose a text-infilling-based approach to generate meta-paths from textual information in HINs. |
| Outcome: | The proposed approach can classify edges in the zero-shot setting, where existing methods cannot generate meta-paths. |
𝒮2IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction (2025.findings-naacl)
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| Challenge: | Aspect Sentiment Quad Prediction (ASQP) is an extractive task that focuses on predicting tuples of sentiment-related elements from a given text. |
| Approach: | They propose a stepwise syntax integration tuning framework that integrates syntactic structure knowledge into LLMs through a multi-step tuning process. |
| Outcome: | The proposed framework integrates syntactic structure knowledge into large language models . it decomposes the quadruple generation task into two stages . the proposed framework significantly improves state-of-the-art performance across multiple datasets . |
Demystifying the Power of Large Language Models in Graph Generation (2025.findings-naacl)
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Yu Wang, Ryan A. Rossi, Namyong Park, Nesreen K. Ahmed, Danai Koutra, Franck Dernoncourt, Tyler Derr
| Challenge: | Large Language Models (LLMs) have been used for graph discriminative tasks, but their potential for graph structure generation remains unexplored. |
| Approach: | They propose to use LLMs to generate graphs that optimize network properties by injecting domain expertise from network science into the code. |
| Outcome: | The proposed model generates graphs satisfying each property in different domains and compares it with established graph generative models across multiple domains. |
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs (2024.findings-emnlp)
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| Challenge: | Existing methods to learn informative data representations on text-attributed hypergraphs struggle to capture full extent of hypergraph structural information and rich linguistic attributes inherent in the nodes attributes. |
| Approach: | They propose to augment a pre-trained BERT model with specialized hypergraph-aware layers for the task of node classification. |
| Outcome: | The proposed model outperforms existing methods on five challenging text-attributed hypergraph node classification benchmarks. |
‘Hello, World!’: Making GNNs Talk with LLMs (2025.findings-emnlp)
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| Challenge: | graph neural networks have shown remarkable performance across diverse graph-related tasks, but their high-dimensional hidden representations render them black boxes. |
| Approach: | They propose a graph-based neural network with hidden representations in the form of human-readable text. |
| Outcome: | The proposed GNN outperforms existing LLM-based baseline methods on node classification and link prediction. |
Embedding Dynamic Attributed Networks by Modeling the Evolution Processes (2020.coling-main)
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| Challenge: | Existing methods to embed nodes into low-dimensional vectors focus on static networks, but in practice, many networks are evolving over time and hence are dynamic, e.g., social networks. |
| Approach: | They propose to extract high-order neighborhood information at each given timestamp and then use an embedding prediction framework to capture the temporal correlations. |
| Outcome: | Extensive experiments on four real-world datasets show that the proposed method outperforms baseline methods for dynamic link prediction and node classification tasks. |
Can Large Language Models Tackle Graph Partitioning? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have remarkable capabilities in understanding complex tasks, but they can only handle graph partitioning tasks that require global perception abilities. |
| Approach: | They propose a pipeline for coarsening, reasoning, and refining to enable LLMs to perform graph partitioning on small-scale graphs. |
| Outcome: | The proposed pipeline can handle graph partitioning tasks on small graphs with coarsening, reasoning, and refining. |
AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing agentic frameworks treat external information as unstructured text and fail to leverage topological dependencies inherent in real-world data. |
| Approach: | They propose to reframe graph learning as an interleaved process of topology-aware navigation and LLM-based inference. |
| Outcome: | The proposed framework outperforms strong GraphLLMs and GraphRAG benchmarks in multiple LLM backbones. |
Graph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning (2025.emnlp-main)
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| 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. |
GraphAgent: Agentic Graph Language Assistant (2025.emnlp-main)
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| Challenge: | Real-world data combines structured and unstructured formats, capturing explicit relationships and implicit semantic interdependencies. |
| Approach: | They propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive and generative tasks. |
| Outcome: | Extensive experiments on diverse datasets validate GraphAgent’s effectiveness in graph-related predictive and text generative tasks. |
GASE: Graph-Aware Semantic Embedding Learning with Frozen LLMs for Text-Attributed Graphs (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown strong potential for text-attributed graph (TAG) learning, yet effectively integrating LLM semantics with graph structural information remains challenging. |
| Approach: | They propose a framework for learning Graph-Aware Semantic Embeddings using frozen LLMs. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on node classification and achieves a 5 speedup over fine-tuning-based methods. |
CITE: Benchmarking Heterogeneous Text-Attributed Graph Models (2026.acl-long)
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| Challenge: | Recent advances in large language models and text-aware graph learning have increased interest in reasoning over text-attributed graphs. |
| Approach: | They propose a large-scale heterogeneous text-attributed graph benchmark for catalytic materials that contains over 438K nodes and 1.2M edges . they establish standardized evaluation protocols for node classification and link prediction and conduct ablation studies to assess the impact of graph heterogenity and textual attributes. |
| Outcome: | The proposed benchmarks are compared to existing methods and provide a baseline for the evaluation of four classes of learning paradigms. |