标题： 少即是多：使用图卷积网络的简单而有效的启发式社区检测 显示英文标题

标题： Less is More: Simple yet Effective Heuristic Community Detection with Graph Convolution Network

摘要： 社区检测在数据挖掘中至关重要。 传统方法主要关注图结构，常常忽视属性特征的重要性。 相比之下，基于深度学习的方法通过对比学习结合属性特征和局部结构信息，提高了检测性能。 然而，现有算法的复杂设计和联合优化使其难以训练，并降低了检测效率。 此外，这些方法需要预先定义社区数量，使得结果容易受到人工干扰。 为了解决这些挑战，我们提出了一种简单而有效的社区检测算法，可以在不依赖数据增强和对比优化的情况下自适应地检测社区。 所提出的算法首先进行社区预检测，以自适应地提取全局结构信息。 然后利用GCN整合局部结构和属性特征。 随后，在特征空间中结合全局、局部结构和属性特征以发现社区归属。 最后，采用模块化最大化方法基于这三种信息优化社区，从而揭示每个节点的社区归属。 我们在各种图数据集上进行了实验比较，将所提出的算法与传统方法和最先进的社区检测算法进行了评估。 实验结果表明，我们的算法在检测速度和效果方面都实现了更高的效率和准确性。 代码可在 https://github.com/wuanghoong/Less-is-More.git 获取。 显示英文摘要

摘要： Community detection is crucial in data mining. Traditional methods primarily focus on graph structure, often neglecting the significance of attribute features. In contrast, deep learning-based approaches incorporate attribute features and local structural information through contrastive learning, improving detection performance. However, existing algorithms' complex design and joint optimization make them difficult to train and reduce detection efficiency. Additionally, these methods require the number of communities to be predefined, making the results susceptible to artificial interference. To address these challenges, we propose a simple yet effective community detection algorithm that can adaptively detect communities without relying on data augmentation and contrastive optimization. The proposed algorithm first performs community pre-detection to extract global structural information adaptively. It then utilizes GCN to integrate local structures and attribute features. Subsequently, it combines global, local structures and attribute features in the feature space to discover community affiliations. Finally, a modularity maximization method is employed to optimize the communities based on these three types of information, thereby uncovering the community affiliation of each node. We conduct experimental comparisons across various graph datasets, evaluating the proposed algorithm against traditional methods and state-of-the-art community detection algorithms. The experimental results demonstrate that our algorithm achieves greater efficiency and accuracy in terms of both detection speed and effectiveness. The code is available at https://github.com/wuanghoong/Less-is-More.git.