Influencer Detection with Dynamic Graph Neural Networks
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Abstract (arXiv)
Leveraging network information for prediction tasks has become a common practice in many domains. Being an important part of targeted marketing, influencer detection can potentially benefit from incorporating dynamic network representation. In this work, we investigate different dynamic Graph Neural Networks (GNNs) configurations for influencer detection and evaluate their prediction performance using a unique corporate data set. We show that using deep multi-head attention in GNN and encoding temporal attributes significantly improves performance. Furthermore, our empirical evaluation illustrates that capturing neighborhood representation is more beneficial that using network centrality measures.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 15 Nov 2022.
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18 of 32 references.
References · 18
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2014DeepWalk: Online Learning of Social Representationscited by 1,543
- 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
- 2017Representation Learning on Graphs: Methods and Applicationscited by 456
6 more
- 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
- 2017Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classificationcited by 40
- 2022Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Surveycited by 14
- 2020Multilayer Network Analysis for Improved Credit Risk Predictioncited by 8
- 2020Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecturecited by 7
- 2020Social network analytics for supervised fraud detection in insurancecited by 7
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Citing papers per year: 2023: 2; 2024: 1.
3 papers.