Modeling text-attributed graphs is a well-known problem due to the difficulty of capturing both the text attribute and the graph structure effectively. Existing models often focus on either the text attribute or the graph structure, potentially neglecting the other aspect. This is primarily because both text learning and graph learning models require significant computational resources, making it impractical to directly connect these models in a series. However, there are situations where text-learning models correctly classify text-attributed nodes, while graph-learning models may classify them incorrectly, and vice versa. To fully leverage the potential of text-attributed graphs, we propose a Coupled Text-attributed Graph Learning (CTGL) framework that combines the strengths of both text-learning and graph-learning models in parallel and avoids the computational cost of serially connecting the two aspect models. Specifically, CTGL introduces coupled text-graph augmentation to enable coupled contrastive learning and facilitate the exchange of valuable information between text learning and graph learning. Experimental results on diverse datasets demonstrate the superior performance of our model compared to state-of-the-art text-learning and graph-learning baselines.

Extreme multi-label text classification (EMTC) involves predicting multiple labels from a vast pool of candidates based on a user’s textual query. While traditional BERT-based methods have shown limited success, large language models (LLMs) have brought new possibilities. It is promising to leverage their remarkable comprehension ability to understand textual queries. However, implementing LLMs is non-trivial for two main reasons. Firstly, real-world EMTC datasets can be extremely large, with candidate product pairs reaching up to ten million in real-world scenarios, which poses significant challenges in data ingestion. Secondly, the large size of LLMs makes computation and memory demands prohibitive for EMTC applications. To this end, we propose QUEST, a Quantized and Efficient Learning with Sampling Technique. QUEST includes a tailored hash sampling module that reduces the data volume to one-fourth of its original size. Additionally, we perform compressive fine-tuning LLMs with only twenty thousand trainable parameters, largely reducing computational requirements. Extensive experiments demonstrate that QUEST outperforms existing methods while requiring fewer computational resources, unlocking efficient EMTC on commodity hardware such as a single Nvidia RTX 3090 GPU with 24 GB of memory.