Robust recommender system: a survey and future directions
With the rapid growth of information, recommender systems have become integral for
providing personalized suggestions and overcoming information overload. However, their …
providing personalized suggestions and overcoming information overload. However, their …
Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks
The proliferation of decentralised electronic healthcare records (EHRs) across medical
institutions requires innovative federated learning strategies for collaborative data analysis …
institutions requires innovative federated learning strategies for collaborative data analysis …
Repeat-Aware Neighbor Sampling for Dynamic Graph Learning
Dynamic graph learning equips the edges with time attributes and allows multiple links
between two nodes, which is a crucial technology for understanding evolving data scenarios …
between two nodes, which is a crucial technology for understanding evolving data scenarios …
Representation Learning of Temporal Graphs with Structural Roles
Temporal graph representation learning has drawn considerable attention in recent years.
Most existing works mainly focus on modeling local structural dependencies of temporal …
Most existing works mainly focus on modeling local structural dependencies of temporal …
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations
and notable for their ease of data acquisition, have garnered considerable attention from …
and notable for their ease of data acquisition, have garnered considerable attention from …
[HTML][HTML] Dynamic Graph Representation Learning for Passenger Behavior Prediction
Passenger behavior prediction aims to track passenger travel patterns through historical
boarding and alighting data, enabling the analysis of urban station passenger flow and …
boarding and alighting data, enabling the analysis of urban station passenger flow and …
Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement
The scientific impact of academic papers is influenced by intricate factors such as dynamic
popularity and inherent contribution. Existing models typically rely on static graphs for …
popularity and inherent contribution. Existing models typically rely on static graphs for …
Input Snapshots Fusion for Scalable Discrete Dynamic Graph Nerual Networks
QG Qi, H Chen, M Cheng, H Liu - arXiv preprint arXiv:2405.06975, 2024 - arxiv.org
Dynamic graphs are ubiquitous in the real world, yet there is a lack of suitable theoretical
frameworks to effectively extend existing static graph models into the temporal domain …
frameworks to effectively extend existing static graph models into the temporal domain …
Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding
Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has
garnered significant research interest, largely due to its powerful capabilities in modeling …
garnered significant research interest, largely due to its powerful capabilities in modeling …
Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs
Heterogeneous graphs have attracted a lot of research interests recently due to the success
for representing complex real-world systems. However, existing methods have two pain …
for representing complex real-world systems. However, existing methods have two pain …