Wu, Weili
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/7069
Weili Wu Is a Professor of Computer Science. Her research interests include:
- Big Data Management and Analysis
- Social Networks
- Database Systems
- Wireless Sensor Networks
- Data Mining
- Spatial Data Mining
- Parallel and Distributed Systems
- Algorithm Design and Analysis
- Bioinformatics
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Browsing Wu, Weili by Author "Li, D."
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Item Marginal Gains to Maximize Content Spread in Social Networks(Institute of Electrical and Electronics Engineers Inc., 2019-05-06) Yang, W.; Ma, J.; Li, Y.; Yan, R.; Yuan, Jing; Wu, Weili; Li, D.; 56851698 (Wu, W); Yuan, Jing; Wu, WeiliThe growing importance of social network for sharing and spreading various contents is leading to the changes in the way of information diffusion. To what extent can social content be diffused highly depends on the size of seed nodes and connectivity of the network. If the seed set is predetermined, then the best way to maximize the content spread is to add connectivities among the users. The existing work shows the content spread maximization problem to be NP-hard. One of the difficulties of designing an effective and efficient algorithm for the content spread maximization problem lies in that the objective function we aim to maximize lacks submodularity. In our work, we formulate the maximize content spread problem from an incremental marginal gain perspective. Although the objective function we derive is not submodular, both submodular lower and upper bounds are constructed and proved. Therefore, we apply the sandwich framework and devise a marginal increment-based algorithm (MIS) that guarantees a data-dependent factor. Furthermore, a novel scalable content spread maximization algorithm influence ranking and fast adjustment (IRFA), which is based on the influence ranking of a single node and fast adjustment with each boosting step in the network, is proposed. Through extensive experiments, we demonstrate that both MIS and IRFA algorithms are effective and outperform other edge selection strategies.Item Maximisation of the Number of β-View Covered Targets in Visual Sensor Networks(Inderscience Enterprises Ltd., 2019-03-24) Guo, L.; Li, D.; Wang, Y.; Zhang, Z.; Tong, Guangmo; Wu, Weili; Du, Dingzhu; 56851698 (Wu, W); 288884264 (Du, D); Tong, Guangmo; Wu, Weili; Du, DingzhuIn some applications using visual sensor networks (VSNs), the facing directions of targets are bounded. Therefore existing full-view coverage (all the facing directions of a target constitutes a disk) is not necessary. We propose a novel model called β-view coverage model through which only necessary facing directions of a target are effectively viewed. This model uses much fewer cameras than those used by full-view coverage model. Based on β-view coverage model, a new problem called β-view covered target maximisation (BVCTM) problem is proposed to maximise the number of β-view covered targets given some fixed and freely rotatable camera sensors. We prove its NP-hardness and transform it into an Integer Linear Programming problem equivalently. Besides, a (1 - e - 1 )-factor approximate algorithm and a camera-utility based greedy algorithm are given for this problem. Finally, we conduct many experiments and investigate the influence of many parameters on these two algorithms. © 2019 Inderscience Enterprises Ltd.