Balancing Spreads of Influence in a Social Network

The personalization of our news consumption on social media has a tendency to reinforce our pre-existing beliefs instead of balancing our opinions. To tackle this issue, Garimella et al. (NIPS'17) modeled the spread of these viewpoints, also called campaigns, using the independent cascade model introduced by Kempe, Kleinberg and Tardos (KDD'03) and studied an optimization problem that aims to balance information exposure when two opposing campaigns propagate in a network. This paper investigates a natural generalization of this optimization problem in which μ different campaigns propagate in the network and we aim to maximize the expected number of nodes that are reached by at least ν or none of the campaigns, where μ ≥ ν ≥ 2. Following Garimella et al., despite this general setting, we also investigate a simplified one, in which campaigns propagate in a correlated manner. While for the simplified setting, we show that the problem can be approximated within a constant factor for any constant μ and ν, for the general setting, we give reductions leading to several approximation hardness results when ν ≥ 3. For instance, assuming the gap exponential time hypothesis to hold, we obtain that the problem cannot be approximated within a factor of n−g(n) for any g(n) = o(1) where n is the number of nodes in the network. We complement our hardness results with an Ω(n−1/2)-approximation algorithm for the general setting when ν = 3 and μ is arbitrary.

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Framework design for competitive marketing mechanism in Iraqi social media

Global Journal of Economic and Business ◽

10.31559/gjeb2021.11.3.9 ◽

2021 ◽

Vol 11 (3) ◽

pp. 452-460

Author(s):

Adil M. Salman ◽

Marwa M.Ismaeel ◽

Israa Ezzat Salem

Keyword(s):

Social Media ◽

Random Network ◽

Cascade Model ◽

Major Objective ◽

Framework Design ◽

Independent Cascade Model ◽

Independent Cascade ◽

Regular Network ◽

Initial Seed ◽

Seed Points

Several organizations in Iraq manufacture similar commodities in this aggressive social trading. The objective of these organizations is diffusing information about their commodities publicly for popularity of the commodities in social media. More returns result in popular commodities and vice versa. The development of a framework incorporating two organizations engaging to broaden the information to the large media has been undertaken. The organizations first identified their initial seed points concurrently and then data was scattered as per the Independent Cascade Model (ICM). The major objective of the organizations is the identification of seed points for the diffusion of data to several points in social media. Significant is also how fast data diffusion can be done. Data effect will arise from either none, one or more nodes in a social interconnection. Evaluation is also accomplished on the number of fraction parts in various sections are affected by the different rates of data diffusion. The simulation result for suggested framework presented better outcomes result for random network 1 and random network 2 comparing with regular network. This framework is used a Hotellingframwork of competition.

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Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5398 ◽

2020 ◽

Vol 34 (01) ◽

pp. 590-597 ◽

Cited By ~ 1

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.

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