计算机科学
杠杆(统计)
网络拓扑
节点(物理)
最优化问题
群落结构
嵌入
复杂网络
图形
数据挖掘
理论计算机科学
机器学习
人工智能
算法
计算机网络
数学
工程类
组合数学
万维网
结构工程
作者
Yixiang Dong,Minnan Luo,Jundong Li,Deng Cai,Qinghua Zheng
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-02-01
卷期号:34 (2): 764-775
被引量:6
标识
DOI:10.1109/tkde.2020.2987784
摘要
Community detection is one of the fundamental tasks in graph mining, which aims to identify group assignment of nodes in a complex network. Recently, network embedding techniques have demonstrated their strong power in advancing the community detection task and achieve better performance than various traditional methods. Despite their empirical success, most of the existing algorithms directly leverage the observed coarse network structure for community detection. Therefore, they often lead to suboptimal performance as the observed connections fail to capture the essential tie strength information among nodes precisely and account for the impact of noisy links. In this paper, an optimal network structure for community detection is introduced to characterize the fine-grained tie strength information between connected nodes and alleviate the adverse effects of noisy links. To obtain an expressive node representation for community detection, we learn the optimal network structure and network embeddings in a joint framework, instead of using a two-stage approach to derive the node embeddings from the coarse network topology. In particular, we formulate the joint framework as an optimization problem and an alternating optimization algorithm is exploited to solve the proposed optimization problem. Additionally, theoretical analyses regarding the computational complexity and the convergence of the optimization algorithm are also provided. Extensive experiments on both synthetic and real-world networks demonstrate the effectiveness and superiority of the proposed framework.
科研通智能强力驱动
Strongly Powered by AbleSci AI