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Found 13 papers, 8 papers with code
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach
Inspired by the recent advances in graph representation learning, we propose to re-sketch the missing modalities problem as a problem of missing graph node features to apply the state-of-the-art feature propagation algorithm eventually.
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.
Edge Directionality Improves Learning on Heterophilic Graphs
Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data.
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.
Learning to Infer Structures of Network Games
We adopt a transformer-like architecture which correctly accounts for the symmetries of the problem and learns a mapping from the equilibrium actions to the network structure of the game without explicit knowledge of the utility function.
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization
In this setting, the agent can take a finite amount of reward-free interactions from a subset of these environments.
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge features of the graph.
Learning to Infer the Structure of Network Games
Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player's payoff depends not only on their actions but also on those of their neighbors.
GRAND: Graph Neural Diffusion
We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.
Tuning Word2vec for Large Scale Recommendation Systems
We show that applying constrained hy-perparameter optimization using only a 10% sample of the data still yields a 91%average improvement in hit rate over the default parameters when applied to thefull datasets.
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems.
SIGN: Scalable Inception Graph Neural Networks
Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.
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ncRNA Classification with Graph Convolutional Networks
Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions.
