Search Results for author:
Found 9 papers, 4 papers with code
All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Furthermore, we visualize the inferred interactions of HIRE to confirm the contribution of our model.
A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment
In this paper, we introduce FGWEA, an unsupervised entity alignment framework that leverages the Fused Gromov-Wasserstein (FGW) distance, allowing for a comprehensive comparison of entity semantics and KG structures within a joint optimization framework.
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
As the graphs to be aligned are usually constructed from different sources, the inconsistency issues of structures and features between two graphs are ubiquitous in real-world applications.
Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Network
The proposed ELGN firstly adds a super node to the 3D complex, and then builds a line graph based on the 3D complex.
Transformer for Graphs: An Overview from Architecture Perspective
In this survey, we provide a comprehensive review of various Graph Transformer models from the architectural design perspective.
Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed
In this paper, we propose Graph Neural Network models for both CS and ACS problems, i. e., Query Driven-GNN and Attributed Query Driven-GNN.
Dirichlet Graph Variational Autoencoder
In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors.
Towards Feature-free TSP Solver Selection: A Deep Learning Approach
To solve TSP efficiently, in addition to developing new TSP solvers, it needs to find a per-instance solver for each TSP instance, which is known as the TSP solver selection problem.
Graph Ordering: Towards the Optimal by Learning
However, regardless of the fruitful progress, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks.
