Deep-Learning-based Identification of Influential Spreaders in Online Social Networks
Deep-Learning-based Identification of Influential Spreaders in Online Social Networks
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DOI：
10.1109/iecon.2019.8927419
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Feng Wang;Jinhua She;Y. Ohyama;Min Wu
中科院分区：
文献类型：
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作者：
Feng Wang;Jinhua She;Y. Ohyama;Min Wu
在线社交网络中有效的有影响力的传播者预测对于诸如病毒式营销、在线推荐等各种网络服务至关重要。传统的机器学习方法主要是在预测模型中采用各种手工特征。然而，它们的有效性严重依赖于领域知识，因此很难将这些模型推广到不同的领域。在本文中，我们提出了一个影响力深度学习（IDL）模型来学习用户的潜在特征表示，以预测影响力传播。我们的IDL模型是完全数据驱动的，它将采样子网络作为深度神经网络的输入，用于学习用户的潜在向量表示。此外，我们设计了一种策略，将用户特定的特征和网络结构融入到图卷积神经网络中，以克服标记训练数据的不平衡问题。实验结果表明，我们的模型优于其他基线在线社交网络，我们的模型是更实用的大规模数据集。
Effective influential spreaders prediction in online social networks is critical for a variety of network services such as viral marketing, online recommendation, and many more. The conventional machine learning methods mainly adopted various hand-crafted features in the prediction models. However, their effectiveness relies on the domain knowledge heavily, and thus it is difficult to generalize these models to different domains. In this paper, we propose an Influence Deep Learning (IDL) model to learn users' latent feature representation for predicting influence spread. Our IDL model is fully data-driven, and it takes sampling subnetworks as input to the deep neural networks for learning users' latent vector representation. Moreover, we design a strategy to incorporate user-specific features and network structure into the graph convolutional neural network to overcome the imbalance problem of labeled training data. The experimental results show that our model outperforms other baselines in online social networks, and our model is more practical in large-scale data sets.