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Found 8 papers, 5 papers with code
Composition-based Multi-Relational Graph Convolutional Networks
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Ranked #22 on
Link Prediction
on FB15k-237
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.
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.
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
In this paper, we analyze how increasing the number of these interactions affects link prediction performance, and utilize our observations to propose InteractE.
Ranked #11 on
Link Prediction
on YAGO3-10
Multitask Learning Strengthens Adversarial Robustness
Although deep networks achieve strong accuracy on a range of computer vision benchmarks, they remain vulnerable to adversarial attacks, where imperceptible input perturbations fool the network.
Link Prediction in Hypergraphs using Graph Convolutional Networks
Additionally, there is need to represent the direction from reactants to products.
SGD on Neural Networks learns Robust Features before Non-Robust
We present our findings in light of other recent results on the evolution of inductive biases learned by neural networks over the course of training.
DIRECT : A Transformer-based Model for Decompiled Identifier Renaming
Decompiling binary executables to high-level code is an important step in reverse engineering scenarios, such as malware analysis and legacy code maintenance.
