Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable network distance browsing in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Categorical skylines for streaming data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mobile call graphs: beyond power-law and lognormal distributions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental tensor analysis: Theory and applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACM Transactions on Knowledge Discovery from Data (TKDD)
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling
Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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Influence maximization is an interesting and well-motivated problem in social networks study. The traditional influence maximization problem is defined as finding the most "influential" vertices without considering the vertex attribute. Though it is useful, in practice, there exist different attributes for vertices, e.g., mobile phone social networks. So, it is more important and useful to capture the vertices having the maximum influence in different search categories, which is exactly the problem that we study in this work. Thus, we name this new problem as Categorical Influence Maximization (CIM). Compare with identifying maximum influence vertices in a single category social network, CIM is much harder because we have to deal with large scale complex data. In this work, based on the observations from real mobile phone social network data, we propose a Probability Distribution based Search method (PDS) to tackle the CIM problem. Specifically, the PDS method consists of three steps. First, we propose a probability distribution based parameter free method (PD-max) to identify the maximum influential vertex set for the specified category by studying the categorical influential distribution within a time interval. Second, among these detected influential vertices, we design a probability distribution based minimizing method (PD-minmax) to find the minimum number of vertices in each category having the maximum influences. We test our solutions with real data sets, which were collected for one year in a city in China. The extensive experiment results show that our methods outperform the existing ones.