research
∙
07/24/2023
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
We investigate various prompting strategies for enhancing personalized r...
share
research
∙
02/28/2022
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Graph Convolutional Network (GCN) plays pivotal roles in many real-world...
research
∙
01/19/2022
Decoupling the Depth and Scope of Graph Neural Networks
State-of-the-art Graph Neural Networks (GNNs) have limited scalability w...
research
∙
11/19/2021
Dynamic Graph Representation Learning via Graph Transformer Networks
Dynamic graph representation learning is an important task with widespre...
research
∙
12/02/2020
Deep Graph Neural Networks with Shallow Subgraph Samplers
While Graph Neural Networks (GNNs) are powerful models for learning repr...
research
∙
11/30/2020
The LDBC Graphalytics Benchmark
In this document, we describe LDBC Graphalytics, an industrial-grade ben...
research
∙
10/30/2020
Revisiting Graph Neural Networks for Link Prediction
Graph neural networks (GNNs) have achieved great success in recent years...
research
∙
11/25/2019
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
Graph kernels are widely used for measuring the similarity between graph...
research
∙
05/25/2018
Scalable Spectral Clustering Using Random Binning Features
Spectral clustering is one of the most effective clustering approaches t...
research
∙
03/14/2018
Local Partition in Rich Graphs
Local graph partitioning is a key graph mining tool that allows research...
research
∙
10/16/2012