A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networksdoi:10.1371/journal.pcbi.1011597MACHINE learningDEEP learningDRUG discoveryMOLECULAR interactionsFUNCTIONAL groupsCHEMICAL structureThe powerful combination of large-scale drug-...
For hypergraph clustering, various methods have been proposed to define hypergraph p-Laplacians in the literature. This work proposes a general framework f
Martin1887/oxigen - Fast, parallel, extensible and adaptable genetic algorithm library. A example using this library solves the N Queens problem for N = 255 in only few seconds and using less than 1 MB of RAM. pkalivas/radiate - A customizable parallel genetic programming engine capable of ...
Hypergraph Survey Hypergraph Learning: Methods and Practices (TPAMI, 2022) [paper] More Recent Advances in (Hyper)Graph Partitioning (ACM Computing Surveys, 2022) [paper] Survey of Hypergraph Neural Networks and Its Application to Action Recognition (CAAI International Conference on Artificial Intelligenc...
【6】 Doubly Smoothed GDA: Global Convergent Algorithm for Constrained Nonconvex-Nonconcave Minimax Optimization标题:双重光滑GDA:约束非凸-非凹极大极小优化的全局收敛算法链接:arxiv.org/abs/2212.1297作者:Taoli Zheng,Linglingzhi Zhu,Anthony Man-Cho So,Jose Blanchet,Jiajin Li机构:The Chinese University ...
There are in general many different places where a rule like this can be applied to a given hypergraph: ✕ So—in a multicomputational fashion—we can define a multiway graph to represent all the possibilities (here starting from {{0,0},{0,0}}): ✕ In our model of ...
HyperSwap is a distributed hyperedge partitioning algorithm designed to improve partitioning performance and quality simultaneously. However, HyperSwap is not as efficient due to the lack of centralized coordination. In this study, we propose a centralized heuristic hypergraph partitioning method based on ...
The analysis of topics in the influence diffusion process allows to improve the advertisement injection phase according to different requirements. Fang et al. (2014) desigend a Topic-sensitive influencer mining (TSIM) by using an hypergraph learning in interest-based social media networks. A topic...
KaHyPar is a multilevel hypergraph partitioning framework for optimizing the cut- and the (λ − 1)-metric. It supports bothrecursive bisectionanddirect k-waypartitioning. As a multilevel algorithm, it consist of three phases: In thecoarsening phase, the hypergraph is coarsened to obtain a hie...
BorutaShap: BorutaShap is a Python library for feature selection that combines two powerful techniques: Boruta algorithm and Shapley values method EvalML: "EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions." -- Its...