  Graph structured data are widely existed and applied in the real-world
applications, while it is a challenge to handling these diverse data and
learning tasks on graph in an efficient manner. When facing the complicated
graph learning tasks, experts have designed diverse Graph Neural Networks
(GNNs) in recent years. They have also implemented AutoML in Graph, also known
as AutoGraph, to automatically generate data-specific solutions. Despite their
success, they encounter limitations in (1) managing diverse learning tasks at
various levels, (2) dealing with different procedures in graph learning beyond
architecture design, and (3) the huge requirements on the prior knowledge when
using AutoGraph. In this paper, we propose to use Large Language Models (LLMs)
as autonomous agents to simplify the learning process on diverse real-world
graphs. Specifically, in response to a user request which may contain varying
data and learning targets at the node, edge, or graph levels, the complex graph
learning task is decomposed into three components following the agent planning,
namely, detecting the learning intent, configuring solutions based on
AutoGraph, and generating a response. The AutoGraph agents manage crucial
procedures in automated graph learning, including data-processing, AutoML
configuration, searching architectures, and hyper-parameter fine-tuning. With
these agents, those components are processed by decomposing and completing step
by step, thereby generating a solution for the given data automatically,
regardless of the learning task on node or graph. The proposed method is dubbed
Auto$^2$Graph, and the comparable performance on different datasets and
learning tasks. Its effectiveness is demonstrated by its comparable performance
on different datasets and learning tasks, as well as the human-like decisions
made by the agents.
