142 citations · 504 across the 29 of their papers we have counts for
14 papers · 1 filter
Nested Graph Neural Networks
Muhan Zhang, Pan Li
Graph neural network (GNN)'s success in graph classification is closely related to the Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features to a…
Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation
Nan Wu, Huake He, Yuan Xie +2
Circuit design is complicated and requires extensive domain-specific expertise. One major obstacle stuck on the way to hardware agile development is the considerably time-consuming…
Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks
Changlin Wan, Muhan Zhang, Wei Hao +3
Hypergraph offers a framework to depict the multilateral relationships in real-world complex data. Predicting higher-order relationships, i.e hyperedge, becomes a fundamental probl…
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Susheel Suresh, Vinith Budde, Jennifer Neville +2
Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon…
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Pan Li, Maofei Que, Zhichao Jiang +2
Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied. T…
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
Pan Li, Zhichao Jiang, Maofei Que +2
Cross domain recommender system constitutes a powerful method to tackle the cold-start and sparsity problem by aggregating and transferring user preferences across multiple categor…