基于Count-Distinct草图的快速自适应影响力最大化算法
Fast and Error-Adaptive Influence Maximization based on Count-Distinct Sketches
摘要：
影响力最大化（IM）是在给定的扩散模型下找到最大化受影响顶点的期望数量的种子顶点集的问题。由于寻找最优种子集的NP困难性，近似算法经常用于IM。在这项工作中，我们描述了一种快速，错误自适应的方法，利用计数-不同的草图和基于哈希的融合采样。为了估计整个扩散中受影响的顶点的数量，我们使用逐顶点Flajolet-Martin草图，其中每个草图对应于一个采样子图。为了有效地模拟扩散，单个顶点的到达集基数以连续的方式存储在内存中。这使得所提出的算法估计的影响顶点的数量在一个单步的模拟一次。对于更快的IM内核，我们只有在观察到超过给定阈值的估计误差后才并行重建草图。我们的实验结果表明，该算法产生高质量的种子集，而高达119倍的速度比最先进的近似算法。此外，它比基于草图的方法快62倍，同时产生的种子集的影响力分数提高了3%-12%
英文摘要：
Influence maximization (IM) is the problem of finding a seed vertex set that maximizes the expected number of vertices influenced under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, approximation algorithms are frequently used for IM. In this work, we describe a fast, error-adaptive approach that leverages Count-Distinct sketches and hash-based fused sampling. To estimate the number of influenced vertices throughout a diffusion, we use per-vertex Flajolet-Martin sketches where each sketch corresponds to a sampled subgraph. To efficiently simulate the diffusions, the reach-set cardinalities of a single vertex are stored in memory in a consecutive fashion. This allows the proposed algorithm to estimate the number of influenced vertices in a single step for simulations at once. For a faster IM kernel, we rebuild the sketches in parallel only after observing estimation errors above a given threshold. Our experimental results show that the proposed algorithm yields high-quality seed sets while being up to 119x faster than a state-of-the-art approximation algorithm. In addition, it is up to 62x faster than a sketch-based approach while producing seed sets with 3%-12% better influence scores
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