Search Results for author: Santiago Segarra

Found 34 papers, 9 papers with code

Link Scheduling using Graph Neural Networks

no code implementations12 Sep 2021 Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

To overcome this limitation, we propose fast heuristics based on graph convolutional networks (GCNs) that can be implemented in centralized and distributed manners.

Robust Hierarchical Clustering for Directed Networks: An Axiomatic Approach

no code implementations16 Aug 2021 Gunnar Carlsson, Facundo Mémoli, Santiago Segarra

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

no code implementations14 Jun 2021 Michael T. Schaub, Jean-Baptiste Seby, Florian Frantzen, T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra

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.

Time Series

Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs

no code implementations31 May 2021 David Ramírez, Antonio G. Marques, Santiago Segarra

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

no code implementations27 May 2021 T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra

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.

Dimensionality Reduction Representation Learning

Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling

no code implementations19 May 2021 Yu Zhu, Ananthram Swami, Santiago Segarra

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.

Link Prediction Node Classification +1

GIST: Distributed Training for Large-Scale Graph Convolutional Networks

1 code implementation20 Feb 2021 Cameron R. Wolfe, Jingkang Yang, Arindam Chowdhury, Chen Dun, Artun Bayer, Santiago Segarra, Anastasios Kyrillidis

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.

Graph Sampling

Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning

1 code implementation19 Feb 2021 Yu Zhu, Boning Li, Santiago Segarra

We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs).

hypergraph partitioning

Principled Simplicial Neural Networks for Trajectory Prediction

1 code implementation19 Feb 2021 T. Mitchell Roddenberry, Nicholas Glaze, Santiago Segarra

We consider the construction of neural network architectures for data on simplicial complexes.

Trajectory Prediction

Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond

no code implementations14 Jan 2021 Michael T. Schaub, Yu Zhu, Jean-Baptiste Seby, T. Mitchell Roddenberry, Santiago Segarra

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

no code implementations24 Dec 2020 Fernando J. Iglesias Garcia, Santiago Segarra, Antonio G. Marques

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

no code implementations17 Dec 2020 T. Mitchell Roddenberry, Santiago Segarra, Anastasios Kyrillidis

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

1 code implementation18 Nov 2020 Abhishek Kumar, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

We study the problem of adaptive contention window (CW) design for random-access wireless networks.


Distributed Scheduling using Graph Neural Networks

1 code implementation18 Nov 2020 Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

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.

Network topology change-point detection from graph signals with prior spectral signatures

no code implementations21 Oct 2020 Chiraag Kaushik, T. Mitchell Roddenberry, Santiago Segarra

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.

Change Point Detection

Joint Inference of Multiple Graphs from Matrix Polynomials

no code implementations16 Oct 2020 Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra

Inferring graph structure from observations on the nodes is an important and popular network science task.

Network Topology Inference with Graphon Spectral Penalties

no code implementations15 Oct 2020 T. Mitchell Roddenberry, Madeline Navarro, Santiago Segarra

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

1 code implementation22 Sep 2020 Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

We study the problem of optimal power allocation in a single-hop ad hoc wireless network.

Signal Processing on Directed Graphs

no code implementations2 Aug 2020 Antonio G. Marques, Santiago Segarra, Gonzalo Mateos

This paper provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs).

Causal Inference

An Underparametrized Deep Decoder Architecture for Graph Signals

no code implementations2 Aug 2019 Samuel Rey, Antonio G. Marques, Santiago Segarra

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.

Denoising Image Compression

Blind identification of stochastic block models from dynamical observations

no code implementations22 May 2019 Michael T. Schaub, Santiago Segarra, John N. Tsitsiklis

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.

Stochastic Block Model

Graph-based Semi-Supervised & Active Learning for Edge Flows

1 code implementation17 May 2019 Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson

The first strategy selects edges to minimize the reconstruction error bound and works well on flows that are approximately divergence-free.

Active Learning

Spectral partitioning of time-varying networks with unobserved edges

no code implementations26 Apr 2019 Michael T. Schaub, Santiago Segarra, Hoi-To Wai

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.

Community Detection

Blind Community Detection from Low-rank Excitations of a Graph Filter

no code implementations5 Sep 2018 Hoi-To Wai, Santiago Segarra, Asuman E. Ozdaglar, Anna Scaglione, Ali Jadbabaie

The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals.

Community Detection

Centrality measures for graphons: Accounting for uncertainty in networks

no code implementations28 Jul 2017 Marco Avella-Medina, Francesca Parise, Michael T. Schaub, Santiago Segarra

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

no code implementations18 Oct 2016 Mark Eisen, Santiago Segarra, Gabriel Egan, Alejandro Ribeiro

We first study the similarity of writing styles between Early English playwrights by comparing the profile WANs.

Hierarchical Clustering of Asymmetric Networks

no code implementations21 Jul 2016 Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra

This paper considers networks where relationships between nodes are represented by directed dissimilarities.

Admissible Hierarchical Clustering Methods and Algorithms for Asymmetric Networks

no code implementations21 Jul 2016 Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra

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

no code implementations21 Jul 2016 Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra

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

no code implementations17 Jun 2014 Santiago Segarra, Mark Eisen, Alejandro Ribeiro

Attribution accuracy is observed to exceed the one achieved by methods that rely on word frequencies alone.

Hierarchical Quasi-Clustering Methods for Asymmetric Networks

no code implementations17 Apr 2014 Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra

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

no code implementations31 Jan 2013 Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra

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.

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