Graph embedding methods such as Graph Neural Networks (GNNs) and Graph
Transformers have contributed to the development of graph reasoning algorithms
for various tasks on knowledge graphs. However, the lack of interpretability
and explainability of graph embedding methods has limited their applicability
in scenarios requiring explicit reasoning. In this paper, we introduce the
Graph Agent (GA), an intelligent agent methodology of leveraging large language
models (LLMs), inductive-deductive reasoning modules, and long-term memory for
knowledge graph reasoning tasks. GA integrates aspects of symbolic reasoning
and existing graph embedding methods to provide an innovative approach for
complex graph reasoning tasks. By converting graph structures into textual
data, GA enables LLMs to process, reason, and provide predictions alongside
human-interpretable explanations. The effectiveness of the GA was evaluated on
node classification and link prediction tasks. Results showed that GA reached
state-of-the-art performance, demonstrating accuracy of 90.65%, 95.48%, and
89.32% on Cora, PubMed, and PrimeKG datasets, respectively. Compared to
existing GNN and transformer models, GA offered advantages of explicit
reasoning ability, free-of-training, easy adaption to various graph reasoning
tasks

通过将图结构转换为文本数据，Graph Agent（GA）将大型语言模型（LLMs）、归纳推理模块和长期记忆与符号推理和现有图嵌入方法相结合，为复杂的图推理任务提供了一种创新方法，实现了与人类可解释的解释一起进行处理、推理和预测，并通过对节点分类和链接预测任务的评估，证明了其在 Cora、PubMed 和 PrimeKG 数据集上达到了最先进的性能，准确率分别为 90.65%、95.48% 和 89.32%。与现有的 GNN 和 Transformer 模型相比，GA 具有显式推理能力、免训练、适应各种图推理任务等优势。

图代理：图的明确推理代理

Graph Agent: Explicit Reasoning Agent for Graphs

Graph embedding methods produce unsupervised node features from graphs that
can then be used for a variety of machine learning tasks. Modern graphs,
particularly in industrial applications, contain billions of nodes and
trillions of edges, which exceeds the capability of existing embedding systems.
We present PyTorch-BigGraph (PBG), an embedding system that incorporates
several modifications to traditional multi-relation embedding systems that
allow it to scale to graphs with billions of nodes and trillions of edges. PBG
uses graph partitioning to train arbitrarily large embeddings on either a
single machine or in a distributed environment. We demonstrate comparable
performance with existing embedding systems on common benchmarks, while
allowing for scaling to arbitrarily large graphs and parallelization on
multiple machines. We train and evaluate embeddings on several large social
network graphs as well as the full Freebase dataset, which contains over 100
million nodes and 2 billion edges.

通过对现有的图嵌入系统进行多方面调整，将图划分为更小的子图进行处理，我们提出了创新型的 PyTorch-BigGraph (PBG) 嵌入系统，其可以扩展到包含数十亿个节点和万亿条边的图谱，使得我们能够对大型社交网络图和 Freebase 数据集等各种大型图谱进行分布式处理和大规模分析。

PyTorch-BigGraph：大规模图嵌入系统

PyTorch-BigGraph: A Large-scale Graph Embedding System

Populating ontology graphs represents a long-standing problem for the
Semantic Web community. Recent advances in translation-based graph embedding
methods for populating instance-level knowledge graphs lead to promising new
approaching for the ontology population problem. However, unlike instance-level
graphs, the majority of relation facts in ontology graphs come with
comprehensive semantic relations, which often include the properties of
transitivity and symmetry, as well as hierarchical relations. These
comprehensive relations are often too complex for existing graph embedding
methods, and direct application of such methods is not feasible. Hence, we
propose On2Vec, a novel translation-based graph embedding method for ontology
population. On2Vec integrates two model components that effectively
characterize comprehensive relation facts in ontology graphs. The first is the
Component-specific Model that encodes concepts and relations into
low-dimensional embedding spaces without a loss of relational properties; the
second is the Hierarchy Model that performs focused learning of hierarchical
relation facts. Experiments on several well-known ontology graphs demonstrate
the promising capabilities of On2Vec in predicting and verifying new relation
facts. These promising results also make possible significant improvements in
related methods.

提出了 On2Vec，它是一种新的基于翻译的图形嵌入方法，可以有效地解决本体填充问题。 On2Vec 集成了两个模型组件，可以有效地特征化本体图中的全面关系事实。 在多个知名本体图上的实验表明，On2Vec 可以预测和验证新的关系事实。