Equivariant Hypergraph Diffusion Neural Operators

Peihao, Wang,; Yang, Shenghao; Liu, Yunyu; Wang, Zhangyang; Li, Pan

doi:10.48550/arxiv.2207.06680

Cited by 2 publications

(2 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the model should also have good extrapolation ability for hyperedges of unseen orders. Inspired by recent works about hypergraph diffusion algorithms, 46,47 The molecular hypergraph is initially transformed into an equivalent bipartite graph [Fig. 2(b)], wherein two distinct sets of vertices denote the nodes and hyperedges of the molecular hypergraph, respectively.…”

Section: B Algorithmmentioning

confidence: 99%

Molecular hypergraph neural networks

Chen,

Schwaller

2024

The Journal of Chemical Physics

View full text Add to dashboard Cite

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as multi-center bonds and conjugated structures. To tackle this challenge, we introduce molecular hypergraphs and propose Molecular Hypergraph Neural Networks (MHNNs) to predict the optoelectronic properties of organic semiconductors, where hyperedges represent conjugated structures. A general algorithm is designed for irregular high-order connections, which can efficiently operate on molecular hypergraphs with hyperedges of various orders. The results show that MHNN outperforms all baseline models on most tasks of organic photovoltaic, OCELOT chromophore v1, and PCQM4Mv2 datasets. Notably, MHNN achieves this without any 3D geometric information, surpassing the baseline model that utilizes atom positions. Moreover, MHNN achieves better performance than pretrained GNNs under limited training data, underscoring its excellent data efficiency. This work provides a new strategy for more general molecular representations and property prediction tasks related to high-order connections.

show abstract

Section: B Algorithmmentioning

confidence: 99%

Molecular hypergraph neural networks

Chen,

Schwaller

2024

The Journal of Chemical Physics

View full text Add to dashboard Cite

show abstract

“…In contrast to MP on plain graphs, this architecture performs two levels of nested aggregations per MP step, since computing x new v requires knowing m e . There are numerous IMP architectures that harness HGs, for example Hypergraph Convolution [15], Hypergraph Neural Networks [97], Hypergraph Attention [15], Hyper-GCN [258], Hypergraph Networks with Hyperedge Neurons [91], Hyper-SAGNN [277], and others [10], [13], [102], [103], [171], [223], [243], [273].…”

Section: Neural Mp On Hypergraphs (Hgs)mentioning

confidence: 99%

Motif Prediction with Graph Neural Networks

Besta¹,

Grob²,

Miglioli³

et al. 2022

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Link prediction is one of the central problems in graph mining. However, recent studies highlight the importance of higher-order network analysis, where complex structures called motifs are the first-class citizens. We first show that existing link prediction schemes fail to effectively predict motifs. To alleviate this, we establish a general motif prediction problem and we propose several heuristics that assess the chances for a specified motif to appear. To make the scores realistic, our heuristics consider -among others -correlations between links, i.e., the potential impact of some arriving links on the appearance of other links in a given motif. Finally, for highest accuracy, we develop a graph neural network (GNN) architecture for motif prediction. Our architecture offers vertex features and sampling schemes that capture the rich structural properties of motifs. While our heuristics are fast and do not need any training, GNNs ensure highest accuracy of predicting motifs, both for dense (e.g., 𝑘-cliques) and for sparse ones (e.g., 𝑘-stars). We consistently outperform the best available competitor by more than 10% on average and up to 32% in area under the curve. Importantly, the advantages of our approach over schemes based on uncorrelated link prediction increase with the increasing motif size and complexity. We also successfully apply our architecture for predicting more arbitrary clusters and communities, illustrating its potential for graph mining beyond motif analysis. CCS CONCEPTS• Information systems → Data mining.

show abstract

Equivariant Hypergraph Diffusion Neural Operators

Cited by 2 publications

References 61 publications

Molecular hypergraph neural networks

Molecular hypergraph neural networks

Motif Prediction with Graph Neural Networks

Contact Info

Product

Resources

About