The field of graph-based methods and optimization techniques is rapidly advancing, with a focus on developing more efficient and effective algorithms for solving complex problems. Recent developments have centered around improving the performance of graph-based approximate nearest neighbor search methods, optimizing the layout of railroad diagrams, and analyzing the structural parameterizations of graph-based problems. Additionally, there have been significant advancements in the development of new algorithms for solving classic problems such as the Maximum Cut problem and the 2-Edge-Connected Spanning Subgraph problem. Noteworthy papers include the proposal of a novel and principled method for selecting the truncation parameter in Sparse Neighborhood Graphs, which achieves comparable or superior performance in terms of query latency and Recall@10 compared to commonly used binary search heuristics. Another notable paper presents a scalable framework for solving the Maximum Cut problem using projected gradient ascent on quadratic objectives, which achieves comparable or superior performance relative to recent training-data-intensive and dataless approaches.
Advances in Graph-Based Methods and Optimization Techniques
Sources
Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification
New constructions of cyclic constant-dimension subspace codes based on Sidon spaces and subspace polynomials
GraphBLAS Mathematical Opportunities: Parallel Hypersparse, Matrix Based Graph Streaming, and Complex-Index Matrices
Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification