2,544 research outputs found
On the Expressive Power of Geometric Graph Neural Networks
The expressive power of Graph Neural Networks (GNNs) has been studied
extensively through the Weisfeiler-Leman (WL) graph isomorphism test. However,
standard GNNs and the WL framework are inapplicable for geometric graphs
embedded in Euclidean space, such as biomolecules, materials, and other
physical systems. In this work, we propose a geometric version of the WL test
(GWL) for discriminating geometric graphs while respecting the underlying
physical symmetries: permutations, rotation, reflection, and translation. We
use GWL to characterise the expressive power of geometric GNNs that are
invariant or equivariant to physical symmetries in terms of distinguishing
geometric graphs. GWL unpacks how key design choices influence geometric GNN
expressivity: (1) Invariant layers have limited expressivity as they cannot
distinguish one-hop identical geometric graphs; (2) Equivariant layers
distinguish a larger class of graphs by propagating geometric information
beyond local neighbourhoods; (3) Higher order tensors and scalarisation enable
maximally powerful geometric GNNs; and (4) GWL's discrimination-based
perspective is equivalent to universal approximation. Synthetic experiments
supplementing our results are available at
https://github.com/chaitjo/geometric-gnn-dojoComment: NeurIPS 2022 Workshop on Symmetry and Geometry in Neural
Representation
From Hypergraph Energy Functions to Hypergraph Neural Networks
Hypergraphs are a powerful abstraction for representing higher-order
interactions between entities of interest. To exploit these relationships in
making downstream predictions, a variety of hypergraph neural network
architectures have recently been proposed, in large part building upon
precursors from the more traditional graph neural network (GNN) literature.
Somewhat differently, in this paper we begin by presenting an expressive family
of parameterized, hypergraph-regularized energy functions. We then demonstrate
how minimizers of these energies effectively serve as node embeddings that,
when paired with a parameterized classifier, can be trained end-to-end via a
supervised bilevel optimization process. Later, we draw parallels between the
implicit architecture of the predictive models emerging from the proposed
bilevel hypergraph optimization, and existing GNN architectures in common use.
Empirically, we demonstrate state-of-the-art results on various hypergraph node
classification benchmarks. Code is available at
https://github.com/yxzwang/PhenomNN.Comment: Accepted to ICML 202
Learning and comparing functional connectomes across subjects
Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes
Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
Fifth-Generation (5G) networks have a potential to accelerate power system
transition to a flexible, softwarized, data-driven, and intelligent grid. With
their evolving support for Machine Learning (ML)/Artificial Intelligence (AI)
functions, 5G networks are expected to enable novel data-centric Smart Grid
(SG) services. In this paper, we explore how data-driven SG services could be
integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus
on the State Estimation (SE) function as a key element of the energy management
system and focus on two main questions. Firstly, in a tutorial fashion, we
present an overview on how distributed SE can be integrated with the elements
of the 5G core network and radio access network architecture. Secondly, we
present and compare two powerful distributed SE methods based on: i) graphical
models and belief propagation, and ii) graph neural networks. We discuss their
performance and capability to support a near real-time distributed SE via 5G
network, taking into account communication delays
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