基于NSGA2的网络环境下多标签种子节点选择

李磊; 楚喻棋; 汪萌; 韩莉; 吴信东

doi:10.11999/JEIT161266

基于NSGA2的网络环境下多标签种子节点选择

doi: 10.11999/JEIT161266

1.
(合肥工业大学计算机与信息学院合肥 230009) ②(科学技术部基础研究管理中心北京 100862) ③(路易斯安那州立大学计算机与信息学院拉斐特 70503 美国)

基金项目:

国家973规划项目(2013CB329604)，国家重点研发计划项目(2016YFB1000901)，国家自然科学基金项目(61503114)

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出版历程
- 收稿日期: 2016-11-24
- 修回日期: 2017-04-11
- 刊出日期: 2017-09-19

NSGA2-based Multi-label Seed Node Selection in Network Environments

1.
(School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China)
2.
(School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China)

Funds:

The National 973 Program of China (2013CB329604), The National Key Research and Development Program of China (2016YFB1000901), The National Natural Science Foundation of China (61503114)

摘要

摘要: 随着社交网络规模的不断扩大，网络节点的标签分类也不再单一，变得丰富多样，这些促使了社交网络中的多标签分类问题成为一个重要的研究领域。以前的研究重点主要集中在提高预测网络节点标签的精度上，而忽略了得到节点信息所产生的包含时间消耗和计算资源等在内的系统开销问题。可现如今随着网络规模不断扩大且复杂性不断增强，之前所忽略的系统开销问题变得越来越严重，增加了预测标签的成本，加重了预测网络节点标签的难度。该文针对这一问题提出了基于NSGA2算法的网络环境下多标签种子节点选择算法(NAMESEA算法)，目的是在能大大降低预测节点标签所消耗的系统开销的前提下一定程度上提高预测标签的精度。该文将NAMESEA算法与其他多标签预测算法在多个真实数据集上进行实验对比，结果证明NAMESEA算法大大降低了预测节点标签的系统开销并且提高了预测精度。
- 社交网络 /
- 多标签分类 /
- NSGA2 /
- 系统开销
Abstract: With the expanding scale of social networks, the label classification of nodes in the network is no longer single but various, which prompts the multi-label classification in social networks to become an important research area. The previous research focuses on how to improve the precision of the predicted labels, while ignoring the system overhead caused by obtaining the node information, such as time consumption and computing memory occupancy. Now, as both expansion and complexity of the networks are increasing, the problem of previously neglected system overhead is becoming the more and the more serious. It increases not only the cost but also the difficulty of predicting labels. In this paper, an NSGA2-based multi-label seed selection algorithm in network environments (NAMESEA) is proposed to improve the accuracy of label prediction on the condition that reducing both the time consume and the memory occupancy. Compared with other multi-label prediction algorithms on multiple real datasets, NAMES
- Social networks /
- Multi-label classification /
- NSGA2 /
- System overhead

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