Graph plays an important role in representing complex relationships in
real-world applications such as social networks, biological data and citation
networks. In recent years, Large Language Models (LLMs) have achieved
tremendous success in various domains, which makes applying LLMs to graphs
particularly appealing. However, directly applying LLMs to graph modalities
presents unique challenges due to the discrepancy and mismatch between the
graph and text modalities. Hence, to further investigate LLMs' potential for
comprehending graph information, we introduce GraphPrompter, a novel framework
designed to align graph information with LLMs via soft prompts. Specifically,
GraphPrompter consists of two main components: a graph neural network to encode
complex graph information and an LLM that effectively processes textual
information. Comprehensive experiments on various benchmark datasets under node
classification and link prediction tasks demonstrate the effectiveness of our
proposed method. The GraphPrompter framework unveils the substantial
capabilities of LLMs as predictors in graph-related tasks, enabling researchers
to utilize LLMs across a spectrum of real-world graph scenarios more
effectively.

通过 GraphPrompter 框架，将 Large Language Models（LLMs）与图形模态对齐，实现了 LLMs 在图相关任务中的重要性和效果。

我们能否将软提示应用于图学习任务的 LLM？

Can we soft prompt LLMs for graph learning tasks?

Complex logical query answering (CLQA) is a recently emerged task of graph
machine learning that goes beyond simple one-hop link prediction and solves a
far more complex task of multi-hop logical reasoning over massive, potentially
incomplete graphs in a latent space. The task received a significant traction
in the community; numerous works expanded the field along theoretical and
practical axes to tackle different types of complex queries and graph
modalities with efficient systems. In this paper, we provide a holistic survey
of CLQA with a detailed taxonomy studying the field from multiple angles,
including graph types (modality, reasoning domain, background semantics),
modeling aspects (encoder, processor, decoder), supported queries (operators,
patterns, projected variables), datasets, evaluation metrics, and applications.
Refining the CLQA task, we introduce the concept of Neural Graph Databases
(NGDBs). Extending the idea of graph databases (graph DBs), NGDB consists of a
Neural Graph Storage and a Neural Graph Engine. Inside Neural Graph Storage, we
design a graph store, a feature store, and further embed information in a
latent embedding store using an encoder. Given a query, Neural Query Engine
learns how to perform query planning and execution in order to efficiently
retrieve the correct results by interacting with the Neural Graph Storage.
Compared with traditional graph DBs, NGDBs allow for a flexible and unified
modeling of features in diverse modalities using the embedding store. Moreover,
when the graph is incomplete, they can provide robust retrieval of answers
which a normal graph DB cannot recover. Finally, we point out promising
directions, unsolved problems and applications of NGDB for future research.

本文综述了复杂逻辑查询应答（CLQA）的最新技术，提出了神经图数据库（NGDB）的概念，介绍了其与传统图数据库的不同之处，并探讨了其未来的研究方向。