Personalized Degrees: Effects on Link Formation in Dynamic Networks from an Egocentric Perspective
Personalized Degrees: Effects on Link Formation in Dynamic Networks from an Egocentric Perspective
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DOI：
10.1145/3308560.3316699
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发表时间：
2017-12
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影响因子：
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通讯作者：
Makan Arastuie;Kevin S. Xu
中科院分区：
文献类型：
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作者：
Makan Arastuie;Kevin S. Xu
了解动态社会网络中驱动链接形成的机制是一个长期存在的问题，它对理解社会结构以及链接预测和推荐具有重要意义。社交网络表现出高度的传递性，这解释了基于邻居的链接预测方法的成功。在本文中，我们从自我节点的角度考察了链接形成背后的机制。我们为自我的每个邻居节点引入了个性化程度的概念，这是一个特定邻居连接到的其他邻居的数量。通过对4个在线社交网络数据集的实证分析，我们发现，无论在有向还是无向环境中，个性化程度较高的邻居在与其他节点作为共同邻居时更容易形成新的链接。这与Adamic和Adar[1]的发现相辅相成，即具有较高（全局）度的相邻节点不太可能导致新的链路形成。此外，在有向网络中，个性化输出度比个性化输入度对链接形成的影响更大，而全局输入度比全局输出度对链接形成的影响更大。我们通过几个链接推荐实验验证了我们的实证研究结果，并观察到将个性化和全局度结合到链接推荐中大大提高了准确性。
Understanding mechanisms driving link formation in dynamic social networks is a long-standing problem that has implications to understanding social structure as well as link prediction and recommendation. Social networks exhibit a high degree of transitivity, which explains the successes of common neighbor-based methods for link prediction. In this paper, we examine mechanisms behind link formation from the perspective of an ego node. We introduce the notion of personalized degree for each neighbor node of the ego, which is the number of other neighbors a particular neighbor is connected to. From empirical analyses on four on-line social network datasets, we find that neighbors with higher personalized degree are more likely to lead to new link formations when they serve as common neighbors with other nodes, both in undirected and directed settings. This is complementary to the finding of Adamic and Adar [1] that neighbor nodes with higher (global) degree are less likely to lead to new link formations. Furthermore, on directed networks, we find that personalized out-degree has a stronger effect on link formation than personalized in-degree, whereas global in-degree has a stronger effect than global out-degree. We validate our empirical findings through several link recommendation experiments and observe that incorporating both personalized and global degree into link recommendation greatly improves accuracy.