Search Results for author:
Found 5 papers, 4 papers with code
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
Specifically, these works propose to accumulate meta-knowledge across diverse meta-training tasks, and then generalize such meta-knowledge to the target task with a disjoint label set.
Empowering Next POI Recommendation with Multi-Relational Modeling
Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations).
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
We then develop a framework EDITS to mitigate the bias in attributed networks while maintaining the performance of GNNs in downstream tasks.
Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US
Besides, as the confounders may be time-varying during COVID-19 (e. g., vigilance of residents changes in the course of the pandemic), it is even more difficult to capture them.
AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
Existing efforts can be mainly categorized as spectral-based and spatial-based methods.
