We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks. GLSM captures the temporal link formation patterns from the observed links with a sequence modeling framework and has the ability to generate the emerging links by inferring from the probability distribution on the potential future links. To avoid overfitting caused by treating each link as a unique token, we propose a self-tokenization mechanism to transform each raw link in the network to an abstract aggregation token automatically. The self-tokenization is seamlessly integrated into the sequence modeling framework, which allows the proposed GLSM model to have the generalization capability to discover link formation patterns beyond raw link sequences. We compare GLSM with the existing state-of-art methods on five real-world datasets. The experimental results demonstrate that GLSM obtains future positive links effectively in a generative fashion while achieving the best performance (2-10% improvements on AUC) among other alternatives.

中文翻译：

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通过自标记序列建模进行生成时间链接预测

我们将具有不断发展的结构的网络形式化为时间网络，并提出了一个生成链接预测模型，即生成链接序列建模（GLSM），以预测时间网络的未来链接。GLSM使用序列建模框架从观察到的链接中捕获时间链接的形成模式，并具有通过从潜在的未来链接上的概率分布推断出生成新链接的能力。为了避免由于将每个链接视为唯一令牌而导致的过拟合，我们提出了一种自令牌化机制，可以将网络中的每个原始链接自动转换为抽象聚合令牌。自标记已无缝集成到序列建模框架中，这使得所提出的GLSM模型具有泛化能力，可以发现原始链接序列以外的链接形成模式。我们将GLSM与五个真实数据集上的现有最先进方法进行了比较。实验结果表明，GLSM以生成方式有效地获得了未来的积极联系，同时在其他替代方案中实现了最佳性能（AUC改善了2-10％）。