Graph Neural Network, which aggre-gates the topological information of the neighbourhoods of each node in a graph to imple-ment graph/network embedding, has attracted wide attention. With the explosive growth of information, large amou...
2024, WSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining A Global Structural Hypergraph Convolutional Model for Bundle Recommendation 2023, Electronics (Switzerland) A Survey of Recommender Systems Based on Hypergraph Neural Networks 2023, Lecture Notes in Com...
The learning architecture of CHESHIRE has four major steps: feature initialization, feature refinement, pooling, and scoring (Fig. 1e, f). For feature initialization, we employ an encoder-based one-layer neural network31 to generate a feature vector for each metabolite from the incidence matrix ...
Notably, we pioneer the application of hypergraph neural networks within the realm of learning-based optimization for general combinatorial optimization problems with higher order constraints. • Enabling scalability to much larger problems by introducing a new distributed and parallel architecture for ...
In: Advances in Neural Information Processing Systems 2006, pp. 801–808 (2006) 5. Ramirez, I., Sprechmann, P., Sapiro, G.: Classification and clustering via dictionary learning with structured incoherence and shared features. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition ...
Kundu et al.’s [22] model constructed a path connecting questions and candidate answers, and then scored them through the neural architecture. Jiang et al. [23] also constructed a proposer to propose an answer from each root to leaf path in the reasoning tree, extract a key sentence ...
Compared to current DSC methods, relying on the self-reconstruction, our method has achieved consistent performance improvement on benchmark high-dimensional datasets. Keywords: deep learning; computational intelligence; neural networks; deep subspace clustering; hypergraph...
Some primitive, basic infrastructure has been built. Huge remaining work items are using neural nets to perform the tensor-like factorization of sheaves, and to redesign the rule-engine to use sheaf-type theorem proving techniques. Building and Installing ...
Rigorous evaluation against established graph kernels, graph neural networks, and graph pooling methods on real-world datasets demonstrates our model’s superior performance, validating its effectiveness in addressing the complexities inherent in heterogeneous graph-level classification....
The spectral analysis methods dominated and most of the research was based on spectral theory before neural networks were introduced to the field of graph representation learning. With the emergence of various deep learning algorithms, researchers have also started to try to extend some deep learning...