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Found 6 papers, 2 papers with code
A Scalable and Effective Alternative to Graph Transformers
Further evaluations on diverse range of benchmarks showcase that GECO scales to large graphs where traditional GTs often face memory and time limitations.
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations.
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature.
Learning to Schedule DAG Tasks
Scheduling computational tasks represented by directed acyclic graphs (DAGs) is challenging because of its complexity.
Variational Optimization for the Submodular Maximum Coverage Problem
We examine the \emph{submodular maximum coverage problem} (SMCP), which is related to a wide range of applications.
Solving Billion-Scale Knapsack Problems
Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale.
