Being an influencer is hard: The complexity of influence maximization in temporal graphs with a fixed source

IF 0.8 4区计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS

Information and Computation Pub Date : 2024-05-08 DOI:10.1016/j.ic.2024.105171

Argyrios Deligkas , Michelle Döring , Eduard Eiben , Tiger-Lily Goldsmith , George Skretas

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引用次数: 0

Abstract

We consider the influence maximization problem over a temporal graph. We deviate from the standard model of influence maximization, where the goal is to choose the most influential vertices. In our model, we are given a fixed vertex and the goal is to find the best time steps to transmit so that the influence of this vertex is maximized. We frame this problem as a spreading process that follows a variant of the susceptible-infected-susceptible (SIS) model and focus on four objective functions. In the MaxSpread objective, the goal is to maximize the number of vertices that get infected at least once. In MaxViral and MaxViralTstep, the goal is to maximize the number of vertices that are infected at the same time step and at a given time step, respectively. Finally, in MinNonViralTime, the goal is to maximize the number of vertices that are infected in every d time-step window.

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成为有影响力的人很困难：具有固定来源的时序图中影响力最大化的复杂性

我们考虑的是时序图上的影响力最大化问题。我们偏离了影响力最大化的标准模型，在该模型中，我们的目标是选择影响力最大的顶点。在我们的模型中，我们给定了一个固定的顶点，目标是找到最佳的传播时间步骤，从而使该顶点的影响力最大化。我们将这个问题设定为一个传播过程，遵循易感-感染-易感（SIS）模型的变体，并重点关注四个目标函数。在 MaxSpread 目标中，目标是使至少被感染一次的顶点数量最大化。在 MaxViral 和 MaxViralTstep 中，目标分别是在同一时间步长和给定时间步长内受感染的顶点数量最大化。最后，在 MinNonViralTime 中，目标是最大化每 d 个时间步窗口中受感染的顶点数量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information and Computation 工程技术-计算机：理论方法

CiteScore

2.30

自引率

0.00%

发文量

119

审稿时长

140 days

期刊介绍： Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking