Leveraging Friendship Networks for Dynamic Link Prediction in Social Interaction Networks
Leveraging Friendship Networks for Dynamic Link Prediction in Social Interaction Networks
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DOI：
10.1609/icwsm.v12i1.15059
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Ruthwik R. Junuthula;Kevin S. Xu;V. Devabhaktuni
中科院分区：
文献类型：
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作者：
Ruthwik R. Junuthula;Kevin S. Xu;V. Devabhaktuni
在线社交网络（OSN）通常包含用户之间的许多不同类型的关系。在研究Facebook等OSN的结构时，最常研究的两个网络是友谊和交互网络。友谊网络中的链接预测问题已经得到了大量的研究。先前也有关于交互网络中的链接预测的工作，独立于友谊网络。在本文中，我们研究了结合友谊和互动网络的预测能力。我们假设，通过利用友谊网络，我们可以提高互动网络中链接预测的准确性。我们增强了几种交互链接预测算法，以纳入友谊和预测的友谊。从Facebook数据的实验中，我们发现，将友谊的互动链接预测算法的结果在更高的准确性，但结合预测的友谊不结合当前的友谊相比。
On-line social networks (OSNs) often contain many different types of relationships between users. When studying the structure of OSNs such as Facebook, two of the most commonly studied networks are friendship and interaction networks. The link prediction problem in friendship networks has been heavily studied. There has also been prior work on link prediction in interaction networks,independent of friendship networks. In this paper, we study the predictive power of combining friendship and interaction networks. We hypothesize that, by leveraging friendship networks, we can improve the accuracy of link prediction in interaction networks. We augment several interaction link prediction algorithms to incorporate friendships and predicted friendships. From experiments on Facebook data, we find that incorporating friendships into interaction link prediction algorithms results in higher accuracy, but incorporating predicted friendships does not when compared to incorporating current friendships.