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

LI Lei; CHU Yuqi; WANG Meng; HAN Li; WU Xindong

doi:10.11999/JEIT161266

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2040-2047

LI Lei, CHU Yuqi, WANG Meng, HAN Li, WU Xindong. NSGA2-based Multi-label Seed Node Selection in Network Environments[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2040-2047. doi: 10.11999/JEIT161266

Citation:

LI Lei, CHU Yuqi, WANG Meng, HAN Li, WU Xindong. NSGA2-based Multi-label Seed Node Selection in Network Environments[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2040-2047. doi: 10.11999/JEIT161266

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

doi: 10.11999/JEIT161266 cstr: 32379.14.JEIT161266

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)

Received Date: 2016-11-24
Rev Recd Date: 2017-04-11
Publish Date: 2017-09-19

Abstract

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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References(32)

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