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Found 6 papers, 2 papers with code
QDC: Quantum Diffusion Convolution Kernels on Graphs
In this work, we propose a new convolution kernel that effectively rewires the graph according to the occupation correlations of the vertices by trading on the generalized diffusion paradigm for the propagation of a quantum particle over the graph.
TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter
We focus on the candidate generation phase of a large-scale ads recommendation problem in this paper, and present a machine learning first heterogeneous re-architecture of this stage which we term TwERC.
Causally-guided Regularization of Graph Attention Improves Generalizability
Graph attention networks estimate the relational importance of node neighbors to aggregate relevant information over local neighborhoods for a prediction task.
Graph Neural Networks for Link Prediction with Subgraph Sketching
Our experiments show that BUDDY also outperforms SGNNs on standard LP benchmarks while being highly scalable and faster than ELPH.
Understanding convolution on graphs via energies
We do so by showing that linear graph convolutions with symmetric weights minimize a multi-particle energy that generalizes the Dirichlet energy; in this setting, the weight matrices induce edge-wise attraction (repulsion) through their positive (negative) eigenvalues, thereby controlling whether the features are being smoothed or sharpened.
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
To this end, we introduce kNN-Embed, a general approach to improving diversity in dense ANN-based retrieval.
