Large models have emerged as the most recent groundbreaking achievements in artificial intelligence, and particularly machine learning. However, when it comes to graphs, large models have not achieved the same level of success as in other fields, such as natural language processing and computer vision. In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models. First, we discuss the desired characteristics of large graph models. Then, we present detailed discussions from three key perspectives: representation basis, graph data, and graph models. In each category, we provide a brief overview of recent advances and highlight the remaining challenges together with our visions. Finally, we discuss valuable applications of large graph models. We believe this perspective paper is able to encourage further investigations into large graph models, ultimately pushing us one step closer towards artificial general intelligence (AGI).

翻译：大模型已成为人工智能，特别是机器学习领域的最新突破性成果。然而，在图数据方面，大模型尚未像在自然语言处理和计算机视觉等其他领域那样取得同等成功。为促进大模型在图数据上的应用，我们撰写此展望论文，探讨开发大图模型面临的挑战与机遇。首先，我们讨论了大图模型应具备的理想特性。随后，我们从三个关键视角进行详细讨论：表示基础、图数据及图模型。在每个类别中，我们简要概述了近期进展，并指出了尚存的挑战及我们的愿景。最后，我们讨论了大图模型的有价值应用。我们相信，本展望论文能够激励对大图模型的进一步研究，最终使我们向通用人工智能（AGI）迈进一步。