# Community-based greedy algorithm for mining top-K influential nodes in mobile social networks

@article{Wang2010CommunitybasedGA, title={Community-based greedy algorithm for mining top-K influential nodes in mobile social networks}, author={Yu Wang and G. Cong and Guojie Song and Kunqing Xie}, journal={Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining}, year={2010} }

With the proliferation of mobile devices and wireless technologies, mobile social network systems are increasingly available. [...] Key Method The proposed algorithm encompasses two components: 1) an algorithm for detecting communities in a social network by taking into account information diffusion; and 2) a dynamic programming algorithm for selecting communities to find influential nodes. We also provide provable approximation guarantees for our algorithm. Empirical studies on a large real-world mobile social… Expand

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#### 482 Citations

Influence Maximization on Large-Scale Mobile Social Network: A Divide-and-Conquer Method

- Computer Science
- IEEE Transactions on Parallel and Distributed Systems
- 2015

A divide-and-conquer strategy with parallel computing mechanism has been adopted and an algorithm called Community-based Greedy algorithm for mining top-K influential nodes and precision analysis is given to show approximation guarantees of the models. Expand

Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm

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- 2015

Experiments result on real large-scale mobile social networks show that the proposed location based greedy algorithm has higher efficiency than previous community greedy algorithm. Expand

A Greedy Algorithm Approach for Mobile Social Network

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- 2015

Experiments on real large-scale mobile social networks show that the proposed algorithm is quicker than previous algorithms, and greedy rule with demonstrable approximation guarantees will provide smart approximation. Expand

Mining Mechanism of Top-k Influential Nodes Based on Voting Algorithm in Mobile Social Networks

- Computer Science
- 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing
- 2013

The complex network theory is introduced to build a social relation graph, which is used to reveal the relationship among people's social contacts and messages sending and intimacy degree is also introduced to characterize social frequency among nodes. Expand

Preference-based mining of top-K influential nodes in social networks

- Computer Science
- Future Gener. Comput. Syst.
- 2014

A two-stage mining algorithm to mine the most influential nodes in a network on a given topic, which shows that GAUP performs better than the state-of-the-art greedy algorithm, SVD-based collaborative filtering, and HITS. Expand

Preference-Based Top-K Influential Nodes Mining in Social Networks

- Computer Science
- 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications
- 2011

This work proposes a two-stage mining algorithm (GAUP) for mining most influential nodes on a specific topic, which uses a collaborative filtering technique to determine user preferences on a topic and adopts a greedy algorithm to find top-K nodes in the network. Expand

An Efficient Influence Maximization Algorithm Based on Social Relationship Priority in Mobile Social Networks

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This paper model the mobile social network as the topological graph based on social priority topological to study the social influence and innovatively propose a scheme which integrates ITO algorithm into PSO algorithm to solve the problem of maximizing the influence in MSNs. Expand

A Hierarchy Based Influence Maximization Algorithm in Social Networks

- Computer Science
- ICANN
- 2018

A new approach called Hierarchy based Influence Maximization algorithm (HBIM in short) to mine top-K influential nodes is proposed, a two-phase method: an algorithm for detecting information diffusion levels based on the first-order and second-order proximity between social nodes and a dynamic programming algorithm for selecting levels to find influential nodes. Expand

An Efficient Influence Maximization Algorithm to Discover Influential Users in Micro-blog

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The Candidates-Based Greedy (CBG) algorithm is proposed, which selects SS from the candidates with a greedy algorithm to maximize the influence in micro-blogging and achieves much better running time, almost 70% less, and does not lose any accuracy. Expand

Dynamic social feature-based diffusion in mobile social networks

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- 2015 IEEE/CIC International Conference on Communications in China (ICCC)
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This paper defines dynamic social features to capture nodes' dynamic contact behavior and use social similarity metrics to measure their social closeness in MSNs and proposes novel diffusion node selection algorithms based on these new features to minimize the diffusion time. Expand

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