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Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach

arXiv 2504.11245 ↗First posted 15 Apr 2025cs.SI cs.AIcited by 0

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Abstract (arXiv)

Influence Maximization (IM) in temporal graphs focuses on identifying influential "seeds" that are pivotal for maximizing network expansion. We advocate defining these seeds through Influence Propagation Paths (IPPs), which is essential for scaling up the network. Our focus lies in efficiently labeling IPPs and accurately predicting these seeds, while addressing the often-overlooked cold-start issue prevalent in temporal networks. Our strategy introduces a motif-based labeling method and a tensorized Temporal Graph Network (TGN) tailored for multi-relational temporal graphs, bolstering prediction accuracy and computational efficiency. Moreover, we augment cold-start nodes with new neighbors from historical data sharing similar IPPs. The recommendation system within an online team-based gaming environment presents subtle impact on the social network, forming multi-relational (i.e., weak and strong) temporal graphs for our empirical IM study. We conduct offline experiments to assess prediction accuracy and model training efficiency, complemented by online A/B testing to validate practical network growth and the effectiveness in addressing the cold-start issue.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 65 references that are in the map. First posted on arXiv 15 Apr 2025.

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18 of 65 references.

References · 18

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2018Datasheets for Datasetscited by 1,386
  3. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  4. 2017struc2vec: Learning Node Representations from Structural Identitycited by 215
  5. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 138
  6. 2018DeepInf: Social Influence Prediction with Deep Learningcited by 128
  7. 2022Towards Better Evaluation for Dynamic Link Predictioncited by 122
  8. 2016Motifs in Temporal Networkscited by 101
  9. 2022Neighborhood-aware Scalable Temporal Network Representation Learningcited by 54
  10. 2020DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendationcited by 46
  11. 2020Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networkscited by 30
  12. 2022A Survey on Influence Maximization: From an ML-Based Combinatorial Optimizationcited by 15
6 more
  1. 2024MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipelinecited by 12
  2. 2021Dynamic Influence Maximizationcited by 6
  3. 2017Influence maximization by rumor spreading on correlated networks through community identificationcited by 3
  4. 2020Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learningcited by 2
  5. 2023Influence maximization on temporal networks: a reviewcited by 1
  6. 2023Fast and Space-Efficient Parallel Algorithms for Influence Maximizationcited by 1

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