Alapfogalmak
The author introduces MIM-Reasoner, a framework that leverages reinforcement learning and probabilistic graphical models to optimize influence spread in multiplex networks.
Kivonat
The MIM-Reasoner framework addresses the challenge of maximizing influence in multiplex networks by decomposing them into layers and using reinforcement learning. It provides theoretical guarantees and achieves competitive performance compared to other state-of-the-art methods. The framework is efficient in terms of training time, inference time, and total spread across both synthetic and real-world datasets.
Key points:
Introduction of MIM-Reasoner for multiplex influence maximization.
Decomposition of multiplex networks into layers for optimization.
Utilization of reinforcement learning and probabilistic graphical models.
Theoretical guarantees provided for the solutions.
Competitive performance demonstrated on synthetic and real-world datasets.
Statisztikák
A multiplex network consists of k layers represented by G = {(G1, σ1), ..., (Gk, σk)}.
Celegans dataset has 6 layers, 3879 nodes, and 8191 edges.
Drosophila dataset has 7 layers, 8215 nodes, and 43,366 edges.
Twitter-Foursquare network has 2 layers, 93269 nodes, and 17,969,114 edges.
Pope-Election dataset has 3 layers, 2,064,866 nodes, and 5,969,189 edges.
Idézetek
"Balancing the model size and computational efficiency is crucial when working with multiplex networks."
"Our Contributions: To overcome both scalability and generalization for lightweight model issues altogether..."
"MIM-Reasoner demonstrates competitive spreading values across all overlapping percentages."