Cross-Network Learning With Fuzzy Labels for Seed Selection and Graph Sparsification in Influence Maximization | IEEE Journals & Magazine | IEEE Xplore

Cross-Network Learning With Fuzzy Labels for Seed Selection and Graph Sparsification in Influence Maximization


Abstract:

To maximize the influence across multiple heterogeneous networks, we propose an innovative cross-network learning model to study the influence maximization problem from t...Show More

Abstract:

To maximize the influence across multiple heterogeneous networks, we propose an innovative cross-network learning model to study the influence maximization problem from two perspectives, namely, seed selection and graph sparsification. On one hand, we consider seed selection as a cross-network node prediction task, by leveraging the greedy seed selection knowledge prelearned in a smaller source network, to heuristically select the nodes most likely to act as seed for the target networks. On the other hand, we consider graph sparsification as a cross-network edge prediction problem, by adapting the influence propagation knowledge previously acquired in the source network to remove the edges least likely to contribute to influence propagation in the target networks. To address domain discrepancy, a fuzzy self-learning algorithm is proposed to iteratively train the prediction model by leveraging not only the fully labeled data in the source network, but also the most confident predicted instances with their predicted fuzzy labels in the target network. With such fuzzy labels, we can differentiate the confident levels of predictions generated by different self-training iterations, thus lowering the negative effects caused by less confident predictions. The performance of the proposed model is benchmarked with the popular influence maximization algorithms for seed selection; and also competed with several graph sparsification algorithms for inactive edge prediction. Experimental results on the real-world datasets show that the proposed cross-network learning model can achieve a good tradeoff between the efficiency and effectiveness of the influence maximization task in the target networks.
Published in: IEEE Transactions on Fuzzy Systems ( Volume: 28, Issue: 9, September 2020)
Page(s): 2195 - 2208
Date of Publication: 25 July 2019

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