Source localization aims to locate information diffusion sources only given the diffusion observation, which has attracted extensive attention in the past few years. Existing methods are mostly tailored for single networks and may not be generalized to handle more complex networks like cross-networks. Cross-network is defined as two interconnected networks, where one network's functionality depends on the other. Source localization on cross-networks entails locating diffusion sources on the source network by only giving the diffused observation in the target network. The task is challenging due to challenges including: 1) diffusion sources distribution modeling; 2) jointly considering both static and dynamic node features; and 3) heterogeneous diffusion patterns learning. In this work, we propose a novel method, namely CNSL, to handle the three primary challenges. Specifically, we propose to learn the distribution of diffusion sources through Bayesian inference and leverage disentangled encoders to separately learn static and dynamic node features. The learning objective is coupled with the cross-network information propagation estimation model to make the inference of diffusion sources considering the overall diffusion process. Additionally, we also provide two novel cross-network datasets collected by ourselves. Extensive experiments are conducted on both datasets to demonstrate the effectiveness of \textit{CNSL} in handling the source localization on cross-networks.

中文翻译：

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跨网络信息传播的源定位

源定位旨在仅根据扩散观测来定位信息扩散源，这在过去几年引起了广泛的关注。现有的方法大多是针对单个网络量身定制的，可能无法推广到处理更复杂的网络（例如跨网络）。跨网络被定义为两个互连的网络，其中一个网络的功能依赖于另一个网络。跨网络上的源定位需要通过仅给出目标网络中的扩散观察来定位源网络上的扩散源。该任务具有挑战性，因为挑战包括：1）扩散源分布建模； 2）共同考虑静态和动态节点特征； 3）异构扩散模式学习。在这项工作中，我们提出了一种新方法，即 CNSL，来应对三个主要挑战。具体来说，我们建议通过贝叶斯推理来学习扩散源的分布，并利用解纠缠编码器来分别学习静态和动态节点特征。学习目标与跨网络信息传播估计模型相结合，以考虑整体传播过程来进行传播源的推断。此外，我们还提供了我们自己收集的两个新颖的跨网络数据集。在这两个数据集上进行了大量的实验，以证明 \textit{CNSL} 在处理跨网络上的源本地化方面的有效性。