  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.
