Deep Learning for Link Prediction in Dynamic Networks Using Weak Estimators
Deep Learning for Link Prediction in Dynamic Networks Using Weak Estimators
复制标题
DOI：
10.1109/access.2018.2845876
复制
发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Zhan, Justin
中科院分区：
文献类型：
--
作者：
Chiu, Carter;Zhan, Justin
链路预测是评估网络中存在边的概率的任务，在许多领域都有重要的应用。传统的方法依赖于在静态上下文中测量两个节点之间的相似性。最近的研究集中在将链接预测扩展到动态环境，预测随着时间的推移而演变的网络中链接的创建和破坏。虽然这是一项艰巨的任务，但深度学习技术的使用已经证明可以显著提高预测的准确性。为此，除了利用传统的相似性度量之外，我们还提出了弱估计器的新应用，以廉价的方式为深度神经网络构建有效的特征向量。由于弱估计器能够估计动态系统中的变化概率，因此已在各种机器学习算法中使用弱估计器来提高模型精度。实验表明，我们的方法在几个真实世界的动态网络的预测精度提高。
Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques has shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate the changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks.