reginf:在任意数量的任意形状查询区域内最大化影响的交互式系统

Hui Li, Q. Yang, Jiangtao Cui
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摘要

近年来,随着基于位置的社交网络的广泛使用,位置感知影响最大化问题在病毒式营销中受到了广泛关注。它的目的是寻找一组种子用户,使其传播的信息能够到达特定地理区域内最大数量的用户。然而,现有的索赔解决方案只能适用于单个简单的查询区域,例如矩形,而不是复杂的查询区域。此外,没有现成的系统供用户直观地处理索赔。本文针对位置感知影响最大化问题提出了两种解决方案。两者都可以处理具有任意数量的区域和任意形状的查询。更重要的是,我们实现了一个基于web的系统,即a2RegInf,它使病毒式营销人员能够在原生GPU支持下直观地解决索赔问题。据我们所知,我们是第一个提供一个现成的系统,通过基于web的界面来回答这个问题,该界面支持任意数量的任意形状的查询区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
a2RegInf: An Interactive System for Maximizing Influence within Arbitrary Number of Arbitrary Shaped Query Regions
Recently, aside with the prevalent usage of location-based social network, location-aware influence maximization (laim) problem has received plenty of attention in viral marketing. It aims to find a set of seed users such that information propagated from them can reach the largest number of users within particular geographical regions. However, existing solutions to laim can only work on single simple query region, e.g., a rectangle, instead of complex ones. Besides, there is no ready-to-use system for users to address laim visually. In this work, we present a pair of solutions towards location-aware influence maximization problem. Both can work on queries with arbitrary number of regions and arbitrary shapes. More importantly, we implement a web-based system, namely a2RegInf, which enables viral marketers to address laim visually, with native GPU support. To the best of our knowledge, we are the first to provide a ready-to-use system for answering the problem over web-based interface that supports arbitrary number of arbitrary shaped query regions.
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