Sequential Attention Source Identification Based on Feature Representation

TL;DR

This paper introduces TGASI, a sequence-to-sequence framework using graph attention for source localization in dynamic scenarios, improving accuracy and scalability without prior knowledge.

Contribution

The paper presents a novel inductive learning-based framework, TGASI, that effectively addresses time-varying interactions in source localization tasks.

Findings

TGASI outperforms state-of-the-art methods in accuracy.

TGASI demonstrates strong scalability to new scenarios.

The temporal attention mechanism improves source importance estimation.

Abstract

Snapshot observation based source localization has been widely studied due to its accessibility and low cost. However, the interaction of users in existing methods does not be addressed in time-varying infection scenarios. So these methods have a decreased accuracy in heterogeneous interaction scenarios. To solve this critical issue, this paper proposes a sequence-to-sequence based localization framework called Temporal-sequence based Graph Attention Source Identification (TGASI) based on an inductive learning idea. More specifically, the encoder focuses on generating multiple features by estimating the influence probability between two users, and the decoder distinguishes the importance of prediction sources in different timestamps by a designed temporal attention mechanism. It's worth mentioning that the inductive learning idea ensures that TGASI can detect the sources in new scenarios without knowing other prior knowledge, which proves the scalability of TGASI. Comprehensive experiments with the SOTA methods demonstrate the higher detection performance and scalability in different scenarios of TGASI.