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GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks

机译:GCN-GAN:加权动态网络的非线性时间链路预测模型

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摘要

In this paper, we generally formulate the dynamics prediction problem of various network systems (e.g., the prediction of mobility, traffic and topology) as the temporal link prediction task. Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks. The proposed model leverages the benefits of the graph convolutional network (GCN), long short-term memory (LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics, topology structure and evolutionary patterns of weighted dynamic networks can be fully exploited to improve the temporal link prediction performance. Concretely, we first utilize GCN to explore the local topological characteristics of each single snapshot and then employ LSTM to characterize the evolving features of the dynamic networks. Moreover, GAN is used to enhance the ability of the model to generate the next weighted network snapshot, which can effectively tackle the sparsity and the wide-value-range problem of edge weights in real-life dynamic networks. To verify the model's effectiveness, we conduct extensive experiments on four datasets of different network systems and application scenarios. The experimental results demonstrate that our model achieves impressive results compared to the state-of-the-art competitors.
机译:在本文中,我们通常将各种网络系统的动态预测问题(例如,移动性,流量和拓扑的预测)表达为时间链路预测任务。与传统的时间链接预测技术忽略了动态网络中潜在的非线性特征和信息链接权重不同,我们引入了一种新型的非线性模型GCN-GAN来解决加权动态网络的挑战性时间链接预测任务。该模型利用了图卷积网络(GCN),长短期记忆(LSTM)以及生成对抗网络(GAN)的优势。因此,可以充分利用加权动态网络的动力学,拓扑结构和演化模式来改善时间链路预测性能。具体而言,我们首先利用GCN探索每个快照的局部拓扑特征,然后利用LSTM表征动态网络的不断发展的特征。此外,GAN用于增强模型生成下一个加权网络快照的能力,可以有效地解决现实动态网络中边缘权重的稀疏性和宽值范围问题。为了验证模型的有效性,我们对不同网络系统和应用场景的四个数据集进行了广泛的实验。实验结果表明,与最新的竞争对手相比,我们的模型取得了令人印象深刻的结果。

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