Conformal Prediction for Multi-Source Detection on a Network

TL;DR

This paper introduces a conformal prediction framework for multi-source detection in networks, providing statistically valid source set estimates with guaranteed recall regardless of diffusion models, and demonstrating superior reliability and scalability.

Contribution

It develops a novel conformal prediction method that offers model-agnostic, statistically valid source detection with efficient computation for large networks.

Findings

Achieves rigorous coverage guarantees in source detection.

Outperforms existing methods in reliability and scalability.

Supports general network diffusion dynamics.

Abstract

Detecting the origin of information or infection spread in networks is a fundamental challenge with applications in misinformation tracking, epidemiology, and beyond. We study the multi-source detection problem: given snapshot observations of node infection status on a graph, estimate the set of source nodes that initiated the propagation. Existing methods either lack statistical guarantees or are limited to specific diffusion models and assumptions. We propose a novel conformal prediction framework that provides statistically valid recall guarantees for source set detection, independent of the underlying diffusion process or data distribution. Our approach introduces principled score functions to quantify the alignment between predicted probabilities and true sources, and leverages a calibration set to construct prediction sets with user-specified recall and coverage levels. The method is applicable to both single- and multi-source scenarios, supports general network diffusion dynamics, and is computationally efficient for large graphs. Empirical results demonstrate that our method achieves rigorous coverage with competitive accuracy, outperforming existing baselines in both reliability and scalability.The code is available online.