A probability-driven structure-aware algorithm for influence maximization under independent cascade model

2021 ◽  
pp. 126318
Author(s):  
Yudong Gong ◽  
Sanyang Liu ◽  
Yiguang Bai
Keyword(s):  
Influence Maximization ◽  
Cascade Model ◽  
Independent Cascade Model ◽  
Independent Cascade

Influence maximization on signed networks under independent cascade model

2018 ◽  
Vol 49 (3) ◽  
pp. 912-928 ◽  
Author(s):  
Wei Liu ◽  
Xin Chen ◽  
Byeungwoo Jeon ◽  
Ling Chen ◽  
Bolun Chen
Keyword(s):  
Influence Maximization ◽  
Cascade Model ◽  
Signed Networks ◽  
Independent Cascade Model ◽  
Independent Cascade

Positive influence maximization in signed social networks under independent cascade model

2020 ◽  
Vol 24 (19) ◽  
pp. 14287-14303 ◽  
Author(s):  
Jun Sheng ◽  
Ling Chen ◽  
Yixin Chen ◽  
Bin Li ◽  
Wei Liu
Keyword(s):  
Social Networks ◽  
Positive Influence ◽  
Influence Maximization ◽  
Cascade Model ◽  
Independent Cascade Model ◽  
Signed Social Networks ◽  
Independent Cascade

scholarly journals Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization

2020 ◽  
Vol 34 (01) ◽  
pp. 590-597 ◽  
Author(s):  
Wei Chen ◽  
Binghui Peng ◽  
Grant Schoenebeck ◽  
Biaoshuai Tao
Keyword(s):  
Greedy Algorithm ◽  
Threshold Model ◽  
Influence Maximization ◽  
Cascade Model ◽  
Maximization Problem ◽  
Expected Number ◽  
Feedback Model ◽  
Linear Threshold ◽  
Independent Cascade Model ◽  
Independent Cascade

We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1-1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a (1-1/e) fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the independent cascade model and the linear threshold model. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a (1-1/e)-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular cascade model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.

scholarly journals Adaptive Influence Maximization in Microblog under the Competitive Independent Cascade Model

2015 ◽  
Vol 1 (2) ◽  
pp. 129-133
Author(s):  
Zheng Ding ◽  
◽  
Kai Niu ◽  
Zhiqiang He ◽  
◽  
...  
Keyword(s):  
Influence Maximization ◽  
Cascade Model ◽  
Independent Cascade Model ◽  
Independent Cascade
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