Sequential Attention Source Identification Based on Feature Representation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderGraph AttentionInductive LearningSimilar Papers 제목 키워드 기반
Neighbor Contextual Information Learners for Joint Intent and Slot Prediction
Intent Identification and Slot Identification aretwo important task for Natural Language Understanding(NLU). Exploration in this areahave gained significance using networks likeRNN, LSTM and GRU. However, modelscontainin…
Natural Language UnderstandingLow-Resource Contextual Topic Identification on Speech
In topic identification (topic ID) on real-world unstructured audio, an audio instance of variable topic shifts is first broken into sequential segments, and each segment is independently classified. We first present a g…
General ClassificationTopic ClassificationTranslationSequential Graph Neural Networks for Source Code Vulnerability Identification
Vulnerability identification constitutes a task of high importance for cyber security. It is quite helpful for locating and fixing vulnerable functions in large applications. However, this task is rather challenging owin…
Graph ClassificationGraph Neural NetworkMulti-View Pre-Trained Model for Code Vulnerability Identification
Vulnerability identification is crucial for cyber security in the software-related industry. Early identification methods require significant manual efforts in crafting features or annotating vulnerable code. Although th…
Contrastive LearningCross Domain Knowledge Transfer for Person Re-identification
Person Re-Identification (re-id) is a challenging task in computer vision, especially when there are limited training data from multiple camera views. In this paper, we pro- pose a deep learning based person re-identific…
AttributeClassificationGeneral ClassificationPerson Re-Identification+1