Current graph clustering methods emphasize individual node and edge con
nections, while ignoring higher-order organization at the level of motif. Re
cently, higher-order graph clustering approaches have been designed by motif
based hypergraphs. However, these approaches often suffer from hypergraph
fragmentation issue seriously, which degrades the clustering performance
greatly. Moreover, real-world graphs usually contain diverse motifs, with nodes
participating in multiple motifs. A key challenge is how to achieve precise
clustering results by integrating information from multiple motifs at the node
level. In this paper, we propose a multi-order graph clustering model (MOGC) to
integrate multiple higher-order structures and edge connections at node level.
MOGC employs an adaptive weight learning mechanism to au tomatically adjust the
contributions of different motifs for each node. This not only tackles
hypergraph fragmentation issue but enhances clustering accuracy. MOGC is
efficiently solved by an alternating minimization algo rithm. Experiments on
seven real-world datasets illustrate the effectiveness of MOGC.

本文提出了一个多阶图聚类模型（MOGC），通过在节点级别整合多个高阶结构和边缘连接，采用自适应权重学习机制来自动调整每个节点不同结构的贡献，从而既解决了超图分割问题，又提高了聚类准确性。通过交替最小化算法高效地解决 MOGC 问题，并在七个真实数据集上的实验结果证明了 MOGC 的有效性。

自适应节点级权重学习的多阶图聚类

Multi-order Graph Clustering with Adaptive Node-level Weight Learning

Exploring rich environments and evaluating one's actions without prior
knowledge is immensely challenging. In this paper, we propose Motif, a general
method to interface such prior knowledge from a Large Language Model (LLM) with
an agent. Motif is based on the idea of grounding LLMs for decision-making
without requiring them to interact with the environment: it elicits preferences
from an LLM over pairs of captions to construct an intrinsic reward, which is
then used to train agents with reinforcement learning. We evaluate Motif's
performance and behavior on the challenging, open-ended and
procedurally-generated NetHack game. Surprisingly, by only learning to maximize
its intrinsic reward, Motif achieves a higher game score than an algorithm
directly trained to maximize the score itself. When combining Motif's intrinsic
reward with the environment reward, our method significantly outperforms
existing approaches and makes progress on tasks where no advancements have ever
been made without demonstrations. Finally, we show that Motif mostly generates
intuitive human-aligned behaviors which can be steered easily through prompt
modifications, while scaling well with the LLM size and the amount of
information given in the prompt.

Motif 是一种基于大型语言模型（LLM）的方法，通过最大化内在奖励来训练代理程序，实现从先验知识中提取环境决策并探索复杂环境的能力，在挑战性的 NetHack 游戏中取得了优异表现并在无需演示的任务上取得了突破。

来自人工智能反馈的内在动机

Motif: Intrinsic Motivation from Artificial Intelligence Feedback

This paper introduces a generalization of Convolutional Neural Networks
(CNNs) to graphs with irregular linkage structures, especially heterogeneous
graphs with typed nodes and schemas. We propose a novel spatial convolution
operation to model the key properties of local connectivity and translation
invariance, using high-order connection patterns or motifs. We develop a novel
deep architecture Motif-CNN that employs an attention model to combine the
features extracted from multiple patterns, thus effectively capturing
high-order structural and feature information. Our experiments on
semi-supervised node classification on real-world social networks and multiple
representative heterogeneous graph datasets indicate significant gains of 6-21%
over existing graph CNNs and other state-of-the-art techniques.

该论文介绍了一种新的卷积神经网络架构，名为 Motif-CNN，能够处理具有不规则连接结构的图形，特别是异构图形，实现了半监督节点分类，并在真实社交网络和多个代表性异构图形数据集上显示出与现有图形卷积神经网络和其他最先进技术相比的显着优势。