Group Link Prediction
Group Link Prediction
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DOI：
10.1109/bigdata47090.2019.9006261
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Andrew Stanhope;Hao Sha;Danielle Barman;M. Hasan;G. Mohler
中科院分区：
文献类型：
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作者：
Andrew Stanhope;Hao Sha;Danielle Barman;M. Hasan;G. Mohler
由于其在社交网络分析、电子商务和推荐系统领域的普遍应用，链接预测的任务在过去十年中受到了数据挖掘和机器学习社区的极大关注。在其原始设置中，该任务仅预测当前未连接的一对实体将来是否会形成连接。然而，在现实生活中，一个实体有时会加入一个群体（或社区），从而与该群体（或社区）建立联系，而不是与个人建立联系。现有的链路预测方案不足以解决这一预测任务。为了克服这一挑战，在这项工作中，我们提出了一个名为群体链接预测的新问题，该问题侧重于评估候选人在给定时间成为群体成员的可能性。这个问题有潜在的应用，比如Facebook或其他社交网络上的友谊或群组建议，以及合著者建议，或群组电子邮件建议。为了解决这个问题，我们提出了一个基于长短期记忆的模型，该模型输入组的嵌入向量并输出候选对象的条件概率分布。我们还介绍了一个集成关键字信息的复合长短期记忆模型。在真实数据集上的实验结果验证了我们提出的模型与各种基线方法的优越性。
Due to its universal applications in the domain of social network analysis, e-commerce, and recommendation systems, the task of link prediction has received enormous attention from the data mining and machine learning communities over the last decade. In its original setting, the task only predicts whether a pair of entities who are not connected at present time will form a connection in future. However, in real-life an entity sometimes join a group (or a community), thus making a connection with the group (or the community), instead of connecting with an individual. Existing solutions to link prediction are inadequate for solving this prediction task. To overcome this challenge, in this work we propose a novel problem named group link prediction which focuses on evaluating the likelihood for a candidate to become a member of a group at a given time. The problem has potential applications such as friendship or group suggestions on Facebook or other social networks, as well as co-authorship suggestion, or group email recommendations. To solve the problem, we propose a Long Short-term Memory based model that inputs the embedding vectors of the group and outputs the conditional probability distributions for the candidates. We also introduce a composite long short-term memory model that integrates keyword information. Experimental results on real-world data sets validate the superiority of our proposed model in comparison to various baseline methods.