Sequential Attention Source Identification Based on Feature
  Representation
Dongpeng Hou Zhen Wang Chao Gao Xuelong Li
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.