• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Node-Grained Incremental Community Detection for Streaming Networks
【24h】

Node-Grained Incremental Community Detection for Streaming Networks

机译：流网络的节点约束增量社区检测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Community detection has been one of the key research topics in the analysis of networked data, which is a powerful tool for understanding organizational structures of complex networks. One major challenge in community detection is to analyze community structures for streaming networks in real-time in which changes arrive sequentially and frequently. The existing incremental algorithms are often designed for edge-grained sequential changes, which are sensitive to the processing sequence of edges. However, there exist many real-world net-works that changes occur on node-grained, i.e., node with its connecting edges is added into network simultaneously and all edges arrive at the same time. In this paper, we propose a novel incremental community detection method based on modularity optimization for node-grained streaming networks. This method takes one vertex and its connecting edges as a processing unit, and equally treats edges involved by same node. Our algorithm is evaluated on a set of real-world networks, and is compared with several representative incremental and non-incremental algorithms. The experimental results show that our method is highly effective for discovering communities in an incremental way. In addition, our algorithm even got better results than Louvain method (the famous modularity optimization algorithm using global information) in some test networks, e.g., citation networks, which are more likely to be node-grained. This may further indicate the significance of the node-grained incremental algorithms.
机译：社区检测一直是网络数据分析中的关键研究主题之一，它是了解复杂网络的组织结构的强大工具。社区检测的一项主要挑战是实时分析流网络的社区结构，在这些结构中，更改会顺序且频繁地到达。现有的增量算法通常设计用于边缘粒度的顺序更改，这些更改对边缘的处理顺序很敏感。但是，现实世界中有许多网络会在节点粒度上发生变化，即，具有连接边缘的节点会同时添加到网络中，并且所有边缘都会同时到达。在本文中，我们提出了一种新的基于模块化优化的增量式社区检测方法，用于节点粒度流网络。该方法将一个顶点及其连接边作为处理单元，并平等地对待同一节点所涉及的边。我们的算法在一组真实世界的网络上进行了评估，并与几种代表性的增量和非增量算法进行了比较。实验结果表明，我们的方法对于以增量方式发现社区非常有效。此外，在某些测试网络（例如引文网络）中，我们的算法甚至比Louvain方法（使用全局信息的著名的模块化优化算法）获得了更好的结果，这些网络更可能是节点粒度的。这可以进一步表明节点粒度增量算法的重要性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号