InfluencerRank:通过图卷积关注递归神经网络发现有效的影响者

Seungbae Kim, Jyun-Yu Jiang, Jinyoung Han, Wei Wang
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引用次数: 0

摘要

由于网红在社交媒体营销中扮演着重要的角色,企业增加了网红营销的预算。在社交网红营销中,雇佣有影响力的人至关重要,但在数亿社交媒体用户中找到合适的网红是一项挑战。在本文中,我们提出了InfluencerRank,根据他们的发布行为和社会关系随时间的变化,根据他们的有效性对影响者进行排名。为了表征网红的发帖行为和社会关系,应用图卷积神经网络对不同历史时期的异质网络网红进行建模。通过学习嵌入节点特征的网络结构,InfluencerRank可以得到每个时期影响者的信息表示。细心的递归神经网络最终通过捕获影响者表征随时间的动态知识,将高效影响者与其他影响者区分开来。在一个Instagram数据集上进行了广泛的实验,该数据集由18,397名网红组成,他们在12个月内发布了2,952,075条帖子。实验结果表明,InfluencerRank优于现有的基线方法。进一步的深入分析表明,我们提出的所有特征和模型组件都有助于发现有效的影响者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks
As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. To represent the posting behaviors and social relations, the graph convolutional neural networks are applied to model influencers with heterogeneous networks during different historical periods. By learning the network structure with the embedded node features, InfluencerRank can derive informative representations for influencers at each period. An attentive recurrent neural network finally distinguishes highly effective influencers from other influencers by capturing the knowledge of the dynamics of influencer representations over time. Extensive experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months. The experimental results demonstrate that InfluencerRank outperforms existing baseline methods. An in-depth analysis further reveals that all of our proposed features and model components are beneficial to discover effective influencers.
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