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Found 16 papers, 11 papers with code
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.
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
In this paper, we use cellular sheaf theory to show that the underlying geometry of the graph is deeply linked with the performance of GNNs in heterophilic settings and their oversmoothing behaviour.
Understanding over-squashing and bottlenecks on graphs via curvature
Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph.
Ranked #43 on
Node Classification
on Citeseer
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.
Beltrami Flow and Neural Diffusion on Graphs
We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE.
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.
Fashion Outfit Generation for E-commerce
Combining items of clothing into an outfit is a major task in fashion retail.
A Recurrent Neural Network Survival Model: Predicting Web User Return Time
We develop a novel RNN survival model that removes the limitations of the state of the art methods.
Predicting Twitter User Socioeconomic Attributes with Network and Language Information
Inferring socioeconomic attributes of social media users such as occupation and income is an important problem in computational social science.
Hybed: Hyperbolic Neural Graph Embedding
Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks.
Generalising Random Forest Parameter Optimisation to Include Stability and Cost
We argue that error reduction is only one of several metrics that must be considered when optimizing random forest parameters for commercial applications.
Neural Embeddings of Graphs in Hyperbolic Space
Neural embeddings have been used with great success in Natural Language Processing (NLP).
Customer Lifetime Value Prediction Using Embeddings
We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling.
Probabilistic Inference of Twitter Users' Age based on What They Follow
Enhancing Twitter data with user ages would advance our ability to study social network structures, information flows and the spread of contagions.
Real-Time Community Detection in Large Social Networks on a Laptop
For a broad range of research, governmental and commercial applications it is important to understand the allegiances, communities and structure of key players in society.
