Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generalization of a traditional graph wh
Hypergraph Partitioning With Embeddings 9 Sep 2019 · Justin Sybrandt, Ruslan Shaydulin, Ilya Safro · Edit social preview Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a ...
Hypergraph Partitioning With Embeddings (TKDE, 2022) [paper] Distributed Hypergraph Processing Using Intersection Graphs (TKDE, 2022) [paper] Data Representation by Joint Hypergraph Embedding and Sparse Coding (TKDE, 2022) [paper] HyperISO: Efficiently Searching Subgraph Containment in Hypergraphs (TKDE,...
Schlag, P. Chan, Multilevel spectral hypergraph partitioning with arbitrary vertex sizes. IEEE Trans. Comput.-Aided Design Integr. Circ. Syst. 18(9), 1389–1399 (1999) 18. C. Yang, R. Wang, S. Yao, T. Abdelzaher, Hypergraph learning with line expansion (2020). Preprint arXiv:...
This model introduces a novel Hyperedge Order Propagation Attention mechanism (HOPA) to compute attention by partitioning hyperedges based on their orders. We aim to measure the influence of hyperedges on its inside nodes and the influence of different nodes on its related hyperedges. By ...
KaHyPar:https://github.com/kahypar/kahypar(Hypergraph Partitioning) HAT:https://github.com/Jpickard1/Hypergraph-Analysis-Toolbox(Hypergraph Analysis) Hypergraph:https://github.com/yamafaktory/hypergraph(Data Structure) Hypergraph Task Hypergraph Embedding:https://paperswithcode.com/task/hypergraph-embedding...
To ensure the comparability of our experimental results with those reported in other studies, we adopted the same data division method as in [18], partitioning each dataset into training, validation, and testing sets with a ratio of 6:2:2. Table 2. Statistics of experimental data. Datasets#...
We first demonstrated the impact of the Mix-n-Match autoencoder (see STAR Methods) by replacing it with the standard paired autoencoder. The model reached similar performance in predicting hyperedges, as expected. We then visualized the learned embeddings by projecting them to two-dimensional ...
Based on this, this author also proposed a random walk-stay scheme as shown in Figure 5, which jointly samples user check-ins and social relationships, and then learns node embeddings from the sampled hypergraphs, not only maintaining the proximity of n-way nodes captured by the hypergraphs, ...