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Found 16 papers, 12 papers with code
Future Directions in Foundations of Graph Machine Learning
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences.
Graph Metanetworks for Processing Diverse Neural Architectures
However, those works developed architectures tailored to specific networks such as MLPs and CNNs without normalization layers, and generalizing such architectures to other types of networks can be challenging.
Expressive Sign Equivariant Networks for Spectral Geometric Learning
In this work, we demonstrate the benefits of sign equivariance for these tasks.
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning
Specifically, in the contrastive learning setting, we introduce an equivariance objective and theoretically prove that its minima forces augmentations on input space to correspond to rotations on the spherical embedding space.
Graph Inductive Biases in Transformers without Message Passing
Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings.
Ranked #1 on
Node Classification
on PATTERN
Equivariant Polynomials for Graph Neural Networks
This paper introduces an alternative expressive power hierarchy based on the ability of GNNs to calculate equivariant polynomials of a certain degree.
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if $v$ is an eigenvector then so is $-v$; and (ii) more general basis symmetries, which occur in higher dimensional eigenspaces with infinitely many choices of basis eigenvectors.
Ranked #10 on
Graph Regression
on ZINC-500k
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other.
Ranked #2 on
Node Classification
on wiki
Graph Learning
Node Classification on Non-Homophilic (Heterophilic) Graphs
Equivariant Subgraph Aggregation Networks
Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture.
Equivariant Manifold Flows
Tractably modelling distributions over manifolds has long been an important goal in the natural sciences.
New Benchmarks for Learning on Non-Homophilous Graphs
Much data with graph structures satisfy the principle of homophily, meaning that connected nodes tend to be similar with respect to a specific attribute.
Ranked #4 on
Node Classification
on Yelp-Fraud
Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results
While message passing Graph Neural Networks (GNNs) have become increasingly popular architectures for learning with graphs, recent works have revealed important shortcomings in their expressive power.
Doubly Stochastic Subspace Clustering
Many state-of-the-art subspace clustering methods follow a two-step process by first constructing an affinity matrix between data points and then applying spectral clustering to this affinity.
Ranked #1 on
Image Clustering
on UMist
Neural Manifold Ordinary Differential Equations
To better conform to data geometry, recent deep generative modelling techniques adapt Euclidean constructions to non-Euclidean spaces.
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform
For example, expertise on song annotations follows a "U shape" where experts are both early and late contributors with non-experts contributing intermediately; we develop a user utility model that captures such behavior.
The Doubly Stochastic Single Eigenvalue Problem: A Computational Approach
The problem of determining $DS_n$, the complex numbers that occur as an eigenvalue of an $n$-by-$n$ doubly stochastic matrix, has been a target of study for some time.
Spectral Theory 15-04, 15A18, 15A29, 15B51
