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Found 61 papers, 30 papers with code
Windowed Fourier Analysis for Signal Processing on Graph Bundles
We consider the task of representing signals supported on graph bundles, which are generalizations of product graphs that allow for "twists" in the product structure.
Unsupervised Learning of Sampling Distributions for Particle Filters
Thus, the crux of particle filters lies in designing sampling distributions that are both easy to sample from and lead to accurate estimators.
Joint graph learning from Gaussian observations in the presence of hidden nodes
Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables.
Delay-aware Backpressure Routing Using Graph Neural Networks
In this work, we improve upon the widely-used metric of hop distance (and its variants) for the shortest path bias by introducing a bias based on the link duty cycle, which we predict using a graph convolutional neural network.
Graph Filters for Signal Processing and Machine Learning on Graphs
For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks.
Beyond Hawkes: Neural Multi-event Forecasting on Spatio-temporal Point Processes
Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases.
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering
Mixup is a data augmentation method to create new training data by linearly interpolating between pairs of data samples and their labels.
Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics
We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic.
Joint Network Topology Inference via a Shared Graphon Model
The proposed joint network and graphon estimation is further enhanced with the introduction of a robust method for noisy graph sampling information.
Hypergraphs with Edge-Dependent Vertex Weights: p-Laplacians and Spectral Clustering
We study p-Laplacians and spectral clustering for a recently proposed hypergraph model that incorporates edge-dependent vertex weights (EDVW).
Enhanced graph-learning schemes driven by similar distributions of motifs
Guided by this, we first assume that we have a reference graph that is related to the sought graph (in the sense of having similar motif densities) and then, we exploit this relation by incorporating a similarity constraint and a regularization term in the network topology inference optimization problem.
Annealed Langevin Dynamics for Massive MIMO Detection
Based on the proposed MIMO detector, we also design a robust version of the method by unfolding and parameterizing one term -- the score of the likelihood -- by a neural network.
Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks
Distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, energy consumption, radio footprint, and security vulnerability.
Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem.
On Local Distributions in Graph Signal Processing
Leveraging this, we are able to seamlessly compare graphs of different sizes and coming from different models, yielding results on the convergence of spectral densities, transferability of filters across arbitrary graphs, and continuity of graph signal properties with respect to the distribution of local substructures.
Graphon-aided Joint Estimation of Multiple Graphs
We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model.
Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks
We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks.
Power Allocation for Wireless Federated Learning using Graph Neural Networks
We propose a data-driven approach for power allocation in the context of federated learning (FL) over interference-limited wireless networks.
Delay-Oriented Distributed Scheduling Using Graph Neural Networks
In wireless multi-hop networks, delay is an important metric for many applications.
Stability Analysis of Unfolded WMMSE for Power Allocation
Power allocation is one of the fundamental problems in wireless networks and a wide variety of algorithms address this problem from different perspectives.
Robust MIMO Detection using Hypernetworks with Learned Regularizers
Our method is based on hypernetworks that generate the parameters of a neural network-based detector that works well on a specific channel.
Label Propagation across Graphs: Node Classification using Graph Neural Tangent Kernels
In this context, our current work considers a challenging inductive setting where a set of labeled graphs are available for training while the unlabeled target graph is completely separate, i. e., there are no connections between labeled and unlabeled nodes.
Unrolling Particles: Unsupervised Learning of Sampling Distributions
Particle filtering is used to compute good nonlinear estimates of complex systems.
Joint inference of multiple graphs with hidden variables from stationary graph signals
Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference.
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters
Graph convolutional neural networks (GCNNs) are popular deep learning architectures that, upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular domains.
Untrained Graph Neural Networks for Denoising
This paper introduces two untrained graph neural network architectures for graph signal denoising, provides theoretical guarantees for their denoising capabilities in a simple setup, and numerically validates the theoretical results in more general scenarios.
Hodgelets: Localized Spectral Representations of Flows on Simplicial Complexes
We first show that the Hodge Laplacian can be used in lieu of the graph Laplacian to construct a family of wavelets for higher-order signals on simplicial complexes.
Link Scheduling using Graph Neural Networks
Test results on medium-sized wireless networks show that our centralized heuristic can reach a near-optimal solution quickly, and our distributed heuristic based on a shallow GCN can reduce by nearly half the suboptimality gap of the distributed greedy solver with minimal increase in complexity.
Robust Hierarchical Clustering for Directed Networks: An Axiomatic Approach
We begin by introducing three practical properties associated with the notion of robustness in hierarchical clustering: linear scale preservation, stability, and excisiveness.
Signal processing on simplicial complexes
Higher-order networks have so far been considered primarily in the context of studying the structure of complex systems, i. e., the higher-order or multi-way relations connecting the constituent entities.
Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs
When either the input or the filter coefficients are known, this is tantamount to assuming that the signals of interest live on a subspace defined by the supporting graph.
An Impossibility Theorem for Node Embedding
With the increasing popularity of graph-based methods for dimensionality reduction and representation learning, node embedding functions have become important objects of study in the literature.
Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling
On the other hand, we propose a matrix factorization method based on a loss function that generalizes that of the skip-gram model with negative sampling to arbitrary similarity matrices.
GIST: Distributed Training for Large-Scale Graph Convolutional Networks
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the number of model parameters.
Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning
We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs).
Principled Simplicial Neural Networks for Trajectory Prediction
We consider the construction of neural network architectures for data on simplicial complexes.
Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
In the context of simplicial complexes, we specifically focus on signal processing using the Hodge Laplacian matrix, a multi-relational operator that leverages the special structure of simplicial complexes and generalizes desirable properties of the Laplacian matrix in graph signal processing.
Blind Demixing of Diffused Graph Signals
Using graphs to model irregular information domains is an effective approach to deal with some of the intricacies of contemporary (network) data.
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets
We study the role of the constraint set in determining the solution to low-rank, positive semidefinite (PSD) matrix sensing problems.
Adaptive Contention Window Design using Deep Q-learning
We study the problem of adaptive contention window (CW) design for random-access wireless networks.
Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks
We study the temporal reconstruction of epidemics evolving over networks.
Distributed Scheduling using Graph Neural Networks
In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.
Efficient power allocation using graph neural networks and deep algorithm unfolding
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
Network topology change-point detection from graph signals with prior spectral signatures
We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to distill the graph topology change-point detection problem to a subspace detection problem.
Joint Inference of Multiple Graphs from Matrix Polynomials
Inferring graph structure from observations on the nodes is an important and popular network science task.
Network Topology Inference with Graphon Spectral Penalties
In particular, we consider the case where the graph was drawn from a graphon model, and we supplement our convex optimization problem with a provably-valid regularizer on the spectrum of the graph to be recovered.
Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
Signal Processing on Directed Graphs
This paper provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs).
An Underparametrized Deep Decoder Architecture for Graph Signals
While deep convolutional architectures have achieved remarkable results in a gamut of supervised applications dealing with images and speech, recent works show that deep untrained non-convolutional architectures can also outperform state-of-the-art methods in several tasks such as image compression and denoising.
Blind identification of stochastic block models from dynamical observations
We consider a blind identification problem in which we aim to recover a statistical model of a network without knowledge of the network's edges, but based solely on nodal observations of a certain process.
Graph-based Semi-Supervised & Active Learning for Edge Flows
The first strategy selects edges to minimize the reconstruction error bound and works well on flows that are approximately divergence-free.
Spectral partitioning of time-varying networks with unobserved edges
We discuss a variant of `blind' community detection, in which we aim to partition an unobserved network from the observation of a (dynamical) graph signal defined on the network.
Blind Community Detection from Low-rank Excitations of a Graph Filter
The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals.
Centrality measures for graphons: Accounting for uncertainty in networks
Using the theory of linear integral operators, we define degree, eigenvector, Katz and PageRank centrality functions for graphons and establish concentration inequalities demonstrating that graphon centrality functions arise naturally as limits of their counterparts defined on sequences of graphs of increasing size.
Stylometric Analysis of Early Modern Period English Plays
We first study the similarity of writing styles between Early English playwrights by comparing the profile WANs.
Hierarchical Clustering of Asymmetric Networks
This paper considers networks where relationships between nodes are represented by directed dissimilarities.
Admissible Hierarchical Clustering Methods and Algorithms for Asymmetric Networks
This paper characterizes hierarchical clustering methods that abide by two previously introduced axioms -- thus, denominated admissible methods -- and proposes tractable algorithms for their implementation.
Excisive Hierarchical Clustering Methods for Network Data
We introduce two practical properties of hierarchical clustering methods for (possibly asymmetric) network data: excisiveness and linear scale preservation.
Authorship Attribution through Function Word Adjacency Networks
Attribution accuracy is observed to exceed the one achieved by methods that rely on word frequencies alone.
Hierarchical Quasi-Clustering Methods for Asymmetric Networks
This paper introduces hierarchical quasi-clustering methods, a generalization of hierarchical clustering for asymmetric networks where the output structure preserves the asymmetry of the input data.
Axiomatic Construction of Hierarchical Clustering in Asymmetric Networks
Our construction of hierarchical clustering methods is based on defining admissible methods to be those methods that abide by the axioms of value - nodes in a network with two nodes are clustered together at the maximum of the two dissimilarities between them - and transformation - when dissimilarities are reduced, the network may become more clustered but not less.
