Search Results for author:
Found 7 papers, 2 papers with code
Let Your Graph Do the Talking: Encoding Structured Data for LLMs
How can we best encode structured data into sequential form for use in large language models (LLMs)?
Talk like a Graph: Encoding Graphs for Large Language Models
Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance.
UGSL: A Unified Framework for Benchmarking Graph Structure Learning
We implement a wide range of existing models in our framework and conduct extensive analyses of the effectiveness of different components in the framework.
HUGE: Huge Unsupervised Graph Embeddings with TPUs
A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges.
Stars: Tera-Scale Graph Building for Clustering and Graph Learning
We evaluate the performance of Stars for clustering and graph learning, and demonstrate 10~1000-fold improvements in pairwise similarity comparisons compared to different baselines, and 2~10-fold improvement in running time without quality loss.
TF-GNN: Graph Neural Networks in TensorFlow
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
Grale: Designing Networks for Graph Learning
Interestingly, there are often many types of similarity available to choose as the edges between nodes, and the choice of edges can drastically affect the performance of downstream semi-supervised learning systems.
