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 Subject "Heuristic algorithms"
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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 Viral Marketing of Online Game by DS Decomposition In Social Networks(Elsevier B.V.) Gao, C.; Du, H.; Wu, Weili; Wang, H.; 56851698 (Wu, W); Wu, WeiliIn social networks, the spread of influence has been studied extensively, but most efforts in existing literature are made on the product used by a single person. This paper attempts to address the product which is used by many persons such as the online game. When multiple people participate in one game, interaction between users is accompanied by browsing and clicking on advertisements, and operators can also earn certain advertising revenues. All these revenues are related to information interaction between people involved in one game. We use game profit to represent all of the revenues gained from players involved in one game and model the game profit maximization problem in social networks, which finds a seed set to maximize the game profit between players who are influenced to buy the game. We prove that the problem is NP-hard and the objective function is neither submodular nor supermodular. To solve it, we decompose it into the Difference between two Submodular functions (DS decomposition) and propose four heuristic algorithms. To address the complexity of computing objective function, we design a new sampling method based on reverse reachable set technology. Experiment results on real datasets show that our approaches perform well. ©2019 Elsevier B.V.