Hi I'm Zhenyu (Fischer) Lei

In this paper, we study an under-explored research problem of inductive forecasting with limited training data, which requires models to generalize the learned spatial-temporal dependencies from the nodes with available training temporal data to those nodes without. To handle this problem, we propose ST-FiT that can achieve superior performance without additional fine-tuning.

while significant progress has been made in understanding brain activity through functional connectivity (FC) graphs, challenges remain in effectively capturing and interpreting the complex, long-range dependencies and multiple pathways that are inherent in these graphs. In this work, we introduce BrainMAP, a novel framework that can extract multiple long-range activation pathways with adaptive sequentialization and pathway aggregation.

we proposed a bot-detection model named BIC. BIC interacts and exchanges information across text modality and graph modality by a text-graph interaction module. BIC contains a semantic consistency module that derives the inconsistency from tweets by the attention weight to identify advanced bots.