This study addresses the challenge of forming effective groups in
collaborative problem-solving environments. Recognizing the complexity of human
interactions and the necessity for efficient collaboration, we propose a novel
approach leveraging graph theory and reinforcement learning. Our methodology
involves constructing a graph from a dataset where nodes represent
participants, and edges signify the interactions between them. We conceptualize
each participant as an agent within a reinforcement learning framework, aiming
to learn an optimal graph structure that reflects effective group dynamics.
Clustering techniques are employed to delineate clear group structures based on
the learned graph. Our approach provides theoretical solutions based on
evaluation metrics and graph measurements, offering insights into potential
improvements in group effectiveness and reductions in conflict incidences. This
research contributes to the fields of collaborative work and educational
psychology by presenting a data-driven, analytical approach to group formation.
It has practical implications for organizational team building, classroom
settings, and any collaborative scenario where group dynamics are crucial. The
study opens new avenues for exploring the application of graph theory and
reinforcement learning in social and behavioral sciences, highlighting the
potential for empirical validation in future work.

本研究通过图论和强化学习提出一种新方法，建立一个数据集的图结构，将参与者作为代理人，学习一个优化的图结构来反映有效的群体动态，并利用聚类技术根据学习到的图划分明确的群体结构。该研究对协作工作和教育心理学领域做出贡献，为组织团队建设、课堂环境和其他需要重视群体动态的协作场景提供实际应用指导，并突显了图论和强化学习在社会和行为科学中的潜力和未来工作的经验验证。

图增强强化学习在协作问题解决中的有效小组形成

Graph Enhanced Reinforcement Learning for Effective Group Formation in  Collaborative Problem Solving

Social instant messaging services are emerging as a transformative form with
which people connect, communicate with friends in their daily life - they
catalyze the formation of social groups, and they bring people stronger sense
of community and connection. However, research community still knows little
about the formation and evolution of groups in the context of social messaging
- their lifecycles, the change in their underlying structures over time, and
the diffusion processes by which they develop new members. In this paper, we
analyze the daily usage logs from WeChat group messaging platform - the largest
standalone messaging communication service in China - with the goal of
understanding the processes by which social messaging groups come together,
grow new members, and evolve over time. Specifically, we discover a strong
dichotomy among groups in terms of their lifecycle, and develop a separability
model by taking into account a broad range of group-level features, showing
that long-term and short-term groups are inherently distinct. We also found
that the lifecycle of messaging groups is largely dependent on their social
roles and functions in users' daily social experiences and specific purposes.
Given the strong separability between the long-term and short-term groups, we
further address the problem concerning the early prediction of successful
communities. In addition to modeling the growth and evolution from group-level
perspective, we investigate the individual-level attributes of group members
and study the diffusion process by which groups gain new members. By
considering members' historical engagement behavior as well as the local social
network structure that they embedded in, we develop a membership cascade model
and demonstrate the effectiveness by achieving AUC of 95.31% in predicting
inviter, and an AUC of 98.66% in predicting invitee.

通过分析中国最大的独立消息通信服务 WeChat 的日常使用日志，本文旨在理解社交消息群组如何相互联系，增长新成员并随着时间的推移发展。我们发现组的生命周期存在很强的二分法，它们的演化在很大程度上取决于它们在用户每日社交体验和特定目的中的社交角色和功能。此外，本文还研究了群成员的个体属性，并研究了群获得新成员的扩散过程，开发了一个会员级联模型并证明了其在预测邀请者和邀请接受者方面的有效性。