GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs

• URL: http://arxiv.org/abs/2503.23713v1
• Date: Mon, 31 Mar 2025 04:28:37 GMT
• Title: GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs
• Authors: Priyanka Gautam, Balasubramaniam Natarajan, Sai Munikoti, S M Ferdous, Mahantesh Halappanavar,
• Abstract summary: We propose a learning-based approach integrating Graph Networks with Bidirectional Long Short-Term Memory (BiLSTM) models.<n>BiLSTM allows our model to analyze patterns from both past and future network states, ensuring adaptability to changes over time.<n>Our method is particularly effective in fields like viral marketing and social network analysis, where understanding temporal dynamics is crucial.
• Score: 3.3853959586196645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an age where information spreads rapidly across social media, effectively identifying influential nodes in dynamic networks is critical. Traditional influence maximization strategies often fail to keep up with rapidly evolving relationships and structures, leading to missed opportunities and inefficiencies. To address this, we propose a novel learning-based approach integrating Graph Neural Networks (GNNs) with Bidirectional Long Short-Term Memory (BiLSTM) models. This hybrid framework captures both structural and temporal dynamics, enabling accurate prediction of candidate nodes for seed set selection. The bidirectional nature of BiLSTM allows our model to analyze patterns from both past and future network states, ensuring adaptability to changes over time. By dynamically adapting to graph evolution at each time snapshot, our approach improves seed set calculation efficiency, achieving an average of 90% accuracy in predicting potential seed nodes across diverse networks. This significantly reduces computational overhead by optimizing the number of nodes evaluated for seed selection. Our method is particularly effective in fields like viral marketing and social network analysis, where understanding temporal dynamics is crucial.

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