Source Localization for Cross Network Information Diffusion

TL;DR

This paper introduces CNSL, a novel method for localizing information diffusion sources across interconnected networks by modeling source distribution, learning node features, and considering the entire diffusion process, validated on new datasets.

Contribution

The paper presents CNSL, a new approach that addresses the challenges of cross-network source localization through Bayesian inference and disentangled feature learning.

Findings

CNSL outperforms existing methods on cross-network datasets.

Effective modeling of diffusion source distribution improves localization accuracy.

The approach handles static and dynamic node features simultaneously.

Abstract

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.