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GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion
In this paper, we address a more challenging task, rumor source detection with incomplete user data, and propose a novel framework, i. e., Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion (GIN-SD), to tackle this challenge.
Advancing Adversarial Robustness Through Adversarial Logit Update
Based on ALU, we introduce a new classification paradigm that utilizes pre- and post-purification logit differences for model's adversarial robustness boost.
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection
For feature engineering, feature selection seems to be an important research content in which is anticipated to select "excellent" features from candidate ones.
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