Time Series Based Link Prediction
Time Series Based Link Prediction
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DOI：
10.1109/ijcnn.2012.6252471
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Paulo Ricardo da Silva Soares;R. Prudêncio
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文献类型：
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作者：
Paulo Ricardo da Silva Soares;R. Prudêncio
链接预测是社交网络分析中的一项任务，它包括预测最有可能出现的连接，考虑社交网络中先前观察到的链接。这一领域的大多数工作只是通过探索网络在特定时刻的状态来预测新链接，而没有考虑链接随着时间的推移而发生的行为。有鉴于此，我们调查，如果时间信息可以带来任何性能增益的链接预测任务。用于链接预测的传统方法使用当前网络的非连接节点对上的所选拓扑相似性度量来获得将由用于链接预测的无监督或监督方法使用的分数。我们的方法最初包括通过计算它们在过去不同时间的相似性得分来为每对非连接节点构建时间序列。然后，我们在这些时间序列上部署一个预测模型，并将其预测值作为对的最终得分。我们的初步结果使用两个链接预测方法（无监督和监督）的合著网络显示令人满意的结果时，时间信息被认为是。
Link prediction is a task in Social Network Analysis that consists of predicting connections that are most likely to appear considering previous observed links in a social network. The majority of works in this area only performs the task by exploring the state of the network at a specific moment to make the prediction of new links, without considering the behavior of links as time goes by. In this light, we investigate if temporal information can bring any performance gain to the link prediction task. A traditional approach for link prediction uses a chosen topological similarity metric on non-connected pairs of nodes of the network at present time to obtain a score that is going to be used by an unsupervised or a supervised method for link prediction. Our approach initially consists of building time series for each pair of non-connected nodes by computing their similarity scores at different past times. Then, we deploy a forecasting model on these time series and use their forecasts as the final scores of the pairs. Our preliminary results using two link prediction methods (unsupervised and supervised) on co-authorship networks revealed satisfactory results when temporal information was considered.