Deep learning approach to link weight prediction
Deep learning approach to link weight prediction
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DOI：
10.1109/ijcnn.2017.7966076
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
Yuchen Hou;L. Holder
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文献类型：
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作者：
Yuchen Hou;L. Holder
深度学习在包括图像识别、语音识别和自然语言处理在内的各个领域都取得了成功。然而，它在图挖掘中的应用研究还处于初级阶段。在这里，我们提出了R模型，这是一种神经网络模型，旨在为链接权重预测问题提供深度学习方法。该模型从已知链路的权值中提取节点的知识，并利用这些知识来预测未知链路的权值。我们通过实验证明了模型R的能力，并将其与随机块模型及其衍生物进行了比较。模型R表明，深度学习可以成功地应用于链接权重预测，在预测精度方面，它比随机块模型及其衍生模型高出73%。我们期望这种新方法为更多的图挖掘任务提供有效的解决方案。
Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to link weight prediction problem. This model extracts knowledge of nodes from known links' weights and uses this knowledge to predict unknown links' weights. We demonstrate the power of Model R through experiments and compare it with stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We anticipate this new approach to provide effective solutions to more graph mining tasks.