S2Aligner decouples semantic and structural components in LLM-as-Aligner pre-training for sparse TAGs and uses structure-oriented reconstruction plus domain risk balancing to improve transferability and reduce generalization gaps.
Taglas: An atlas of text-attributed graph datasets in the era of large graph and language models
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
NOPE coarsens graphs via neighborhood interference rather than selfish pairwise matching to reach linear memory and near-linear time, with NOPE* variant delivering 1.8-10x speedups and comparable or better learning results than full graphs or LLM reasoning.
SSL4RL reformulates self-supervised learning objectives into dense, verifiable reward signals for RL-based fine-tuning of vision-language models, yielding performance gains on reasoning benchmarks.
GSPELL projects GNN embeddings into LLM space and builds hybrid prompts to produce faithful natural-language explanations and sparse subgraphs for GNN predictions on text-attributed graphs.
GLIP is a joint GNN-LLM pretraining framework that uses augmentation, multi-token selection, a diffusion projector, and combined contrastive plus semantic losses to boost graph classification and reasoning after fine-tuning on limited labels.
citing papers explorer
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S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
S2Aligner decouples semantic and structural components in LLM-as-Aligner pre-training for sparse TAGs and uses structure-oriented reconstruction plus domain risk balancing to improve transferability and reduce generalization gaps.
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Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle
NOPE coarsens graphs via neighborhood interference rather than selfish pairwise matching to reach linear memory and near-linear time, with NOPE* variant delivering 1.8-10x speedups and comparable or better learning results than full graphs or LLM reasoning.
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SSL4RL: Revisiting Self-supervised Learning as Intrinsic Reward for Visual-Language Reasoning
SSL4RL reformulates self-supervised learning objectives into dense, verifiable reward signals for RL-based fine-tuning of vision-language models, yielding performance gains on reasoning benchmarks.
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From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
GSPELL projects GNN embeddings into LLM space and builds hybrid prompts to produce faithful natural-language explanations and sparse subgraphs for GNN predictions on text-attributed graphs.
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GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks
GLIP is a joint GNN-LLM pretraining framework that uses augmentation, multi-token selection, a diffusion projector, and combined contrastive plus semantic losses to boost graph classification and reasoning after fine-tuning on limited labels.