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Information Spread Maximization With Multi-Boosting Stages
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AI
DOI：10.1109/TNSE.2022.3185047.png)
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摘要
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Most of the existing works for Influence Maximization (IM) are defined over a ground set. As the natural extension of set space, in recent years, some works have begun to consider IM on lattice space. The lattice boosting problem, however, has not yet been considered. In this paper, we focus on Lattice k-Boosting Problem. Because each edge has multi-boosting stages, it is an obstacle to estimate the value of boosting influence function by conventional sampling technique. To overcome this obstacle, we adopt the largest boosting probability of each edge to generate Potentially Reverse Reachable graph (PRR graph) and propose a boosting parameter of each boosting edge to amend it. Additionally, it is an unbiased estimation of boosting influence. Because Lattice k-Boosting Problem is not diminishing returns submodular (DR-submodular), we simplify Sandwich Approach to Semi Sandwich Approach by finding a tight DR-submodular lower bound, which also keeps a data-driven approximation ratio. Besides, we design a heuristic algorithm, namely Ktop Algorithm, to return a feasible solution of the original problem. Numerical experiments show that., under the same constraint, selecting each node with the stronger boosting power rather than many nodes may have large boasting influences.
Keyword:
Boosting influence maximization
lattice space
non-DR-submodular
Semi-Sandwich approach
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7.9
论文数:
2.6K
被引数:
10.0K
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