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.

翻译：社交媒体营销中，影响力者发挥着重要作用，因此企业不断增加影响力者营销预算。在社交影响力者营销中，筛选高效影响力者至关重要，但要从数亿社交媒体用户中精准定位合适人选仍极具挑战性。本文提出InfluencerRank模型，通过分析影响力者随时间变化的发帖行为与社交关系，对其有效性进行排序。为表征发帖行为与社交关系，我们采用图卷积神经网络对不同历史时期的影响力者构建异质网络模型。通过学习嵌入节点特征的网络结构，InfluencerRank能为每个时期的影响力者生成信息丰富的表示向量。注意力循环神经网络通过捕捉影响力者表征随时间演变的动态特征，最终实现对高效影响力者的精准区分。基于包含18,397名影响力者及其12个月内发布的2,952,075条帖子的Instagram数据集实验表明，InfluencerRank显著优于现有基线方法。深度分析进一步验证，本文提出的所有特征与模型组件均有助于发现高效影响力者。