Learning Parameters for Balanced Index Influence Maximization

• URL: http://arxiv.org/abs/2012.08067v1
• Date: Tue, 15 Dec 2020 03:30:54 GMT
• Title: Learning Parameters for Balanced Index Influence Maximization
• Authors: Manqing Ma, Gyorgy Korniss, Boleslaw K. Szymanski
• Abstract summary: We focus on a it Balance Index algorithm that relies on three parameters to tune its performance to the given network structure. We create small snapshots from the given synthetic and large-scale real-world networks. We train our machine-learning model on the snapshots and apply this model to the real-word network to find the best BI parameters.
• Score: 0.45119235878273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Influence maximization is the task of finding the smallest set of nodes whose activation in a social network can trigger an activation cascade that reaches the targeted network coverage, where threshold rules determine the outcome of influence. This problem is NP-hard and it has generated a significant amount of recent research on finding efficient heuristics. We focus on a {\it Balance Index} algorithm that relies on three parameters to tune its performance to the given network structure. We propose using a supervised machine-learning approach for such tuning. We select the most influential graph features for the parameter tuning. Then, using random-walk-based graph-sampling, we create small snapshots from the given synthetic and large-scale real-world networks. Using exhaustive search, we find for these snapshots the high accuracy values of BI parameters to use as a ground truth. Then, we train our machine-learning model on the snapshots and apply this model to the real-word network to find the best BI parameters. We apply these parameters to the sampled real-world network to measure the quality of the sets of initiators found this way. We use various real-world networks to validate our approach against other heuristic.

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