Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades

TL;DR

This paper presents a two-stage framework for source identification in diffusion networks from incomplete traces, effectively pinpointing the original source and initiation time with higher accuracy than previous methods.

Contribution

The paper introduces a novel two-stage approach combining continuous-time diffusion modeling and likelihood maximization for source identification in partially observed cascades.

Findings

Outperforms previous methods in synthetic data experiments

Accurately identifies source nodes and initiation times in real-world data

Demonstrates robustness to incomplete diffusion traces

Abstract

When a piece of malicious information becomes rampant in an information diffusion network, can we identify the source node that originally introduced the piece into the network and infer the time when it initiated this? Being able to do so is critical for curtailing the spread of malicious information, and reducing the potential losses incurred. This is a very challenging problem since typically only incomplete traces are observed and we need to unroll the incomplete traces into the past in order to pinpoint the source. In this paper, we tackle this problem by developing a two-stage framework, which first learns a continuous-time diffusion network model based on historical diffusion traces and then identifies the source of an incomplete diffusion trace by maximizing the likelihood of the trace under the learned model. Experiments on both large synthetic and real-world data show that our framework can effectively go back to the past, and pinpoint the source node and its initiation time significantly more accurately than previous state-of-the-arts.