论文标题
随时间变化的图表表示通过高阶跳动和负抽样学习
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling
论文作者
论文摘要
图表的表示模型是成功的技术系列，它们将节点投射到特征空间中，这些空间可以被其他机器学习算法利用。由于许多现实世界的网络本质上是动态的，因此节点之间的相互作用随时间变化，因此可以为静态图和随时间变化的图定义这些技术。在这里，我们基于以下事实：跳过嵌入方法隐式执行矩阵分解，并将其扩展为在时间变化图的不同张量表示上执行隐张张量分解。我们表明，具有负抽样（HOSGNS）的高阶跳过，能够解散节点和时间的作用，而其他方法所需的参数数量的一小部分。我们使用时间分辨的面对面接近数据对我们的方法进行经验评估，这表明，用于求解诸如网络重建之类的下游任务时，学到的时变图表表示优于最先进的方法，并预测诸如疾病扩散等动态过程的结果。源代码和数据可在https://github.com/simonepiaggesi/hosgns上公开获取。
Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. Since many real-world networks are inherently dynamic, with interactions among nodes changing over time, these techniques can be defined both for static and for time-varying graphs. Here, we build upon the fact that the skip-gram embedding approach implicitly performs a matrix factorization, and we extend it to perform implicit tensor factorization on different tensor representations of time-varying graphs. We show that higher-order skip-gram with negative sampling (HOSGNS) is able to disentangle the role of nodes and time, with a small fraction of the number of parameters needed by other approaches. We empirically evaluate our approach using time-resolved face-to-face proximity data, showing that the learned time-varying graph representations outperform state-of-the-art methods when used to solve downstream tasks such as network reconstruction, and to predict the outcome of dynamical processes such as disease spreading. The source code and data are publicly available at https://github.com/simonepiaggesi/hosgns.
