Text-attributed graphs (TAGs) are prevalent in various real-world applications, including academic networks, e-commerce platforms, and social networks. Effective learning on TAGs requires leveraging both textual node features and structural graph information. While language models (LMs) excel at processing text and graph neural networks (GNNs) effectively capture relational structures, their direct integration is computationally prohibitive due to the high cost of text and graph representation learning. Existing approaches address this challenge by adopting a two-step pipeline where LMs generate fixed node embeddings, which are then used for GNN training. However, this method neglects the interaction between textual and structural information, leading to suboptimal learning outcomes. To overcome these limitations, we propose SKETCH (Semantic Knowledge and Structure Enrichment), a novel framework that decouples node aggregation from graph convolution and integrates it into the text representation learning process. SKETCH enhances TAG learning by incorporating two key aggregation mechanisms: (1) Semantic aggregation, which retrieves semantically relevant node texts for contextual enrichment, and (2) Structural aggregation, which propagates textual features beyond immediate neighbors to capture broader graph relationships. Extensive experiments demonstrate that SKETCH outperforms state-of-the-art TAG learning methods while requiring fewer computational resources. By enabling a more efficient and effective fusion of textual and structural information, SKETCH provides new insights into TAG problems and offers a practical solution for real applications.

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.

Natural language has been extensively used for modeling text-attributed graphs with LLMs. Natural language is used to describe the graph for LLMs to understand or serve as component of the graph, e.g., textual attributes for embedding generation. However, natural language is inherently redundant and unstructured, making it unsuitable for modeling high-order neighbors with LLMs. Specifically, (i) graph descriptions become verbose, overwhelming LLMs, and (ii) only relying on attribute embeddings limits LLM’s ability to capture the adequate graph structural information. These limitations make it difficult to model graphs both concisely and adequately using sole natural language with LLMs.Inspired by the observation that LLMs pre-trained on one language can achieve exceptional performance on another with minimal additional training, we propose Graph-Defined Language for Large Language Model (GDL4LLM). This novel framework enables LLMs to transfer their powerful language understanding capabilities to graph-structured data. GDL4LLM translates the graph into a graph language corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. This corpus represents the subgraph centered around target nodes concisely with only a few tokens during fine-tuning on downstream tasks. By treating the graph as a new language, GDL4LLM enables LLMs to model text-attributed graph adequately and concisely. Extensive experiments on five datasets demonstrate that GDL4LLM outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors.

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.