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Found 10 papers, 6 papers with code
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks
SPHH is consist of two self-supervised pretraining tasks that aim to simultaneously learn both local and global representations of the entities in the hypergraph by using informative representations derived from the hypergraph structure.
Graph-based Modeling of Online Communities for Fake News Detection
Existing research has modeled the structure, style, content, and patterns in dissemination of online posts, as well as the demographic traits of users who interact with them.
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
Link Prediction in Hypergraphs using Graph Convolutional Networks
Additionally, there is need to represent the direction from reactants to products.
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
Lovasz Convolutional Networks
We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs.
Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information
Besides the tensors, in many real world scenarios, side information is also available in the form of matrices which also grow in size with time.
A dual framework for low-rank tensor completion
One of the popular approaches for low-rank tensor completion is to use the latent trace norm regularization.
Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation
Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text.
Relation Schema Induction using Tensor Factorization with Side Information
To the best of our knowledge, this is the first application of tensor factorization for the RSI problem.
