142 citations · 504 across the 29 of their papers we have counts for
18 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…
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…
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Susheel Suresh, Pan Li, Cong Hao +1
Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data. Graph contrastive learning…
Local Hyper-Flow Diffusion
Kimon Fountoulakis, Pan Li, Shenghao Yang
Recently, hypergraphs have attracted a lot of attention due to their ability to capture complex relations among entities. The insurgence of hypergraphs has resulted in data of incr…
Handling many conversions per click in modeling delayed feedback
Ashwinkumar Badanidiyuru, Andrew Evdokimov, Vinodh Krishnan +3
Predicting the expected value or number of post-click conversions (purchases or other events) is a key task in performance-based digital advertising. In training a conversion optim…