论文标题
用于分类不断发展网络中传播的时间图形内核
A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks
论文作者
论文摘要
我们介绍了\ emph {permal graphlet kernel}，以分类标记的时间图中的传播过程。这种传播过程可以在动态网络中传播（假）新闻，传染病或计算机病毒。网络被建模为标记的时间图，其中边缘在特定时间点存在，并且节点标签随时间变化。分类问题要求区分不同起源或参数的传播过程，例如具有不同感染概率的传染病。我们的新内核代表了时间图的特征空间中标记的时间图，即以其结构，时间依赖性节点标签和边缘的时间顺序分辨的小子图。我们基于有效可计数的图形类别介绍了内核的变体。对于时间楔形，我们提出了一个高效的近似核，预期误差较低。我们表明，与最先进的方法相比，我们的内核更快地计算和提供了更好的准确性。
We introduce the \emph{temporal graphlet kernel} for classifying dissemination processes in labeled temporal graphs. Such dissemination processes can be spreading (fake) news, infectious diseases, or computer viruses in dynamic networks. The networks are modeled as labeled temporal graphs, in which the edges exist at specific points in time, and node labels change over time. The classification problem asks to discriminate dissemination processes of different origins or parameters, e.g., infectious diseases with different infection probabilities. Our new kernel represents labeled temporal graphs in the feature space of temporal graphlets, i.e., small subgraphs distinguished by their structure, time-dependent node labels, and chronological order of edges. We introduce variants of our kernel based on classes of graphlets that are efficiently countable. For the case of temporal wedges, we propose a highly efficient approximative kernel with low error in expectation. We show that our kernels are faster to compute and provide better accuracy than state-of-the-art methods.
