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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Breaking the Barrier to Transferring Link Information across Networks
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Breaking the Barrier to Transferring Link Information across Networks

机译：打破跨网络传输链接信息的障碍

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摘要

Link prediction is one of the most fundamental problems in graph modeling and mining. It has been studied in a wide range of scenarios, from uncovering missing links between different entities in databases, to recommending relations between people in social networks. In this problem, we wish to predict unseen links in a growing target network by exploiting existing structures in source networks. Most of the existing methods often assume that abundant links are available in the target network to build a model for link prediction. However, in many scenarios, the target network may be too sparse to enable robust inference process, which makes link prediction challenging with the paucity of link data. On the other hand, in many cases, other (more densely linked) auxiliary networks can be available that contains similar link structure relevant to that in the target network. The linkage information in the existing networks can be used in both networks in order to make more accurate link recommendations. Thus, this paper proposes the use of learning methods to perform link inference by transferring the link information from the source network to the target network. We also note that the source network may contain the link information irrelevant to the target network. This leads to cross-network bias between the networks, which makes the link model built upon the source network misaligned with the link structure of the target network. Therefore, we re-sample the source network to rectify such cross-network bias by maximizing the cross-network relevance measured by the node attributes, as well as preserving as rich link information as possible to avoid the loss of source link structure caused by the re-sampling algorithm. The link model based on the re-sampled source network can make more accurate link predictions on the target network with aligned link structures across the networks. We present experimenta- results illustrating the effectiveness of the approach.
机译：链接预测是图建模和挖掘中最基本的问题之一。从发现数据库中不同实体之间缺少的链接到推荐社交网络中人与人之间的关系的广泛场景中进行了研究。在此问题中，我们希望通过利用源网络中的现有结构来预测不断增长的目标网络中看不见的链接。大多数现有方法通常假定目标网络中有大量链接可用，以建立链接预测模型。但是，在许多情况下，目标网络可能太稀疏，无法实现可靠的推理过程，这使得链路预测面临着链路数据匮乏的挑战。另一方面，在许多情况下，可以使用其他（链接更紧密的）辅助网络，其中包含与目标网络相关的相似链接结构。现有网络中的链接信息可以在两个网络中使用，以便做出更准确的链接建议。因此，本文提出使用学习方法通​​过将链接信息从源网络传输到目标网络来执行链接推理。我们还注意到，源网络可能包含与目标网络无关的链接信息。这导致网络之间的跨网络偏差，这使得建立在源网络上的链路模型与目标网络的链路结构不一致。因此，我们通过最大化节点属性测得的跨网络相关性，对源网络进行重新采样，以纠正这种跨网络偏差，并尽可能保留丰富的链接信息，以避免由网络造成的源链接结构损失。重采样算法。基于重新采样的源网络的链接模型可以在目标网络上使用跨网络的对齐链接结构进行更准确的链接预测。我们提供的实验结果说明了该方法的有效性。

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