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Influence Maximization in Dynamic Social Networks
所属机构名称:中国科学院计算技术研究所
会议名称:The IEEE International Conference on Data Mining series (ICDM 2013)
时间:2013
成果类型:会议
相关项目:理论计算机科学
同会议论文项目
理论计算机科学
期刊论文 17
会议论文 64
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Tighter relations between sensitivity and other complexity measures
How to select the top k elements from evolving data?
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On the communication complexity of linear algebraic problems in the message passing model
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Computing the Least-Core and Nucleolus for Threshold Cardinality Matching Games
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Minimizing seed set selection with probabilistic coverage guarantee in a social network
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Solving multi-choice secretary problem in parallel: An optimal observation-selection protocol
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On the communication complexity of linear algebraic problems in the message passing model
Optimal Pricing for Improving Efficiency of Taxi Systems
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Influence Maximization in Dynamic Social Networks
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Space-bounded communication complexity
Determinantal Complexities and Field Extensions
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Any Monotone Property of 3-Uniform Hypergraphs Is Weakly Evasive
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Optimal Pricing for Improving Efficiency of Taxi Systems
Tighter relations between sensitivity and other complexity measures
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Space-bounded communication complexity
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Learning Market Parameters using Aggregate Demand Queries
Minimizing seed set selection with probabilistic coverage guarantee in a social network
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Multi-classes Feature Engineering with Sliding Window for Purchase Prediction in Mobile Commerce
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Learning Market Parameters using Aggregate Demand Queries
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Influence Maximization in Dynamic Social Networks
The least-core and nucleolus of path cooperative games
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On the power of parity queries in boolean decision trees
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Multi-classes Feature Engineering with Sliding Window for Purchase Prediction in Mobile Commerce
Determinantal Complexities and Field Extensions
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Any Monotone Property of 3-Uniform Hypergraphs Is Weakly Evasive
Any Monotone Property of 3-Uniform Hypergraphs Is Weakly Evasive
Tighter relations between sensitivity and other complexity measures
The least-core and nucleolus of path cooperative games
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On the power of parity queries in boolean decision trees
On the power of parity queries in boolean decision trees