44 citations · 136 across the 13 of their papers we have counts for
13 papers
QDC: Quantum Diffusion Convolution Kernels on Graphs
Thomas Markovich
Graph convolutional neural networks (GCNs) operate by aggregating messages over local neighborhoods given the prediction task under interest. Many GCNs can be understood as a form…
TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter
Vanessa Cai, Pradeep Prabakar, Manuel Serrano Rebuelta +8
Recommendation systems are a core feature of social media companies with their uses including recommending organic and promoted contents. Many modern recommendation systems are spl…
Causally-guided Regularization of Graph Attention Improves Generalizability
Alexander P. Wu, Thomas Markovich, Bonnie Berger +2
Graph attention networks estimate the relational importance of node neighbors to aggregate relevant information over local neighborhoods for a prediction task. However, the inferre…
Graph Neural Networks for Link Prediction with Subgraph Sketching
Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi +5
Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link Prediction (LP) tasks. This is due to limitations in expressive power such as the inability t…
Understanding convolution on graphs via energies
Francesco Di Giovanni, James Rowbottom, Benjamin P. Chamberlain +2
Graph Neural Networks (GNNs) typically operate by message-passing, where the state of a node is updated based on the information received from its neighbours. Most message-passing…
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
Ahmed El-Kishky, Thomas Markovich, Kenny Leung +3
Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate item…