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Found 7 papers, 6 papers with code
Stochastic Subgraph Neighborhood Pooling for Subgraph Classification
To address the scalability issue while maintaining generalization, we propose Stochastic Subgraph Neighborhood Pooling (SSNP), which jointly aggregates the subgraph and its neighborhood (i. e., external topology) information without any computationally expensive operations such as labeling tricks.
Simplifying Subgraph Representation Learning for Scalable Link Prediction
Link prediction on graphs is a fundamental problem.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Sampling Enclosing Subgraphs for Link Prediction
Graph neural networks have offered robust solutions for this problem, specifically by learning the representation of the subgraph enclosing the target link (i. e., pair of nodes).
Improving Peer Assessment with Graph Convolutional Networks
In this work, we first model peer assessment as multi-relational weighted networks that can express a variety of peer assessment setups, plus capture conflicts of interest and strategic behaviors.
DeepGroup: Representation Learning for Group Recommendation with Implicit Feedback
These two problems are of interest to not only group recommendation, but also to personal privacy when the users intend to conceal their personal preferences but have participated in group decisions.
Distilling Knowledge via Intermediate Classifiers
However, when there is a large difference between the model complexities of teacher and student (i. e., capacity gap), knowledge distillation loses its strength in transferring knowledge from the teacher to the student, thus training a weaker student.
Joint Variational Autoencoders for Recommendation with Implicit Feedback
We introduce joint variational autoencoders (JoVA), an ensemble of two VAEs, in which VAEs jointly learn both user and item representations and collectively reconstruct and predict user preferences.
