Inspired by the success of Large Language Models (LLMs) in language and vision tasks, there has been growing interest in applying LLMs to graph tasks, particularly on Text-Attributed Graphs (TAGs). However, most prior work tackles the node classification task. In this work, we evaluate an LLM's ability to reason over structured data and infer new facts based on learned patterns by focusing on link prediction (LP)-the task of predicting missing links between nodes-that is understudied in the literature. This task poses two key challenges: (1) How to effectively integrate pairwise structural information, which is crucial for LP performance, into LLMs, and (2) how to address the computational bottleneck during inference. To tackle these challenges, we propose LinkGPT, the first LLM-based training and inference framework specifically designed for LP on homogeneous TAGs. To enhance the LLM's ability to understand the underlying structure, we carefully design a node encoder and pairwise encoder, and leverage a two-stage instruction tuning to effectively incorporate the nodewise and pairwise information into LLMs. For inference efficiency, we introduce a retrieval-reranking scheme. Extensive experiments show that LinkGPT achieves state-of-the-art performance on real-world graphs and demonstrates superior zero-shot and few-shot generalization. At inference time, it achieves a 10× speedup while maintaining high LP accuracy.

Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in downstream tasks remains an underexplored area. To address this critical gap, we introduce the Multimodal Graph Benchmark (MM-Graph), a pioneering benchmark that incorporates both visual and textual information into graph learning tasks. MM-Graph extends beyond existing text-attributed graph benchmarks, offering a more comprehensive evaluation framework for multimodal graph learning. Our benchmark comprises seven diverse datasets of varying scales (ranging from thousands to millions of edges), designed to assess algorithms across different tasks in real-world scenarios. These datasets feature rich multimodal node attributes, including visual data, which enables a more holistic evaluation of various graph learning frameworks in complex, multimodal environments. To support advancements in this emerging field, we provide an extensive empirical study on various graph learning frameworks when presented with features from multiple modalities, particularly emphasizing the impact of visual information. This study offers valuable insights into the challenges and opportunities of integrating visual data into graph learning.

Large language models (LLMs) have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience with the objective of advancing research and applications in this field. To this end, we present the first-ever LLM in geoscience, K2, alongside a suite of resources developed to further promote LLM research within geoscience. For instance, we have curated the first geoscience instruction tuning dataset, GeoSignal, which aims to align LLM responses to geoscience-related user queries. Additionally, we have established the first geoscience benchmark, GeoBench, to evaluate LLMs in the context of geoscience. In this work, we experiment with a complete recipe to adapt a pre-trained general-domain LLM to the geoscience domain. Specifically, we further train the LLaMA-7B model on 5.5B tokens of geoscience text corpus, including over 1 million pieces of geoscience literature, and utilize GeoSignal's supervised data to fine-tune the model. Moreover, we share a protocol that can efficiently gather domain-specific data and construct domain-supervised data, even in situations where manpower is scarce. Meanwhile, we equip K2 with the abilities of using tools to be a naive geoscience aide. Experiments conducted on the GeoBench demonstrate the effectiveness of our approach and datasets on geoscience knowledge understanding and utilization.We open-source all the training data and K2 model checkpoints at https://github.com/davendw49/k2