Publications by Augusto José Rabelo Almeida Santos

Local Tomography of Large Networks Under the Low-Observability Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of partial observations, wh ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Learning Graph Influence From Social Interactions

Ali H. Sayed, Augusto José Rabelo Almeida Santos, Virginia Bordignon

In social learning, agents form their opinions or beliefs about certain hypotheses by exchanging local information. This work considers the recent paradigm of weak graphs, where the network is partitioned into sending and receiving components, with the for ...

IEEE2020

Interplay Between Topology and Social Learning Over Weak Graphs

Ali H. Sayed, Augusto José Rabelo Almeida Santos, Virginia Bordignon

This work examines a distributed learning problem where the agents of a network form their beliefs about certain hypotheses of interest. Each agent collects streaming (private) data and updates continually its belief by means of a diffusion strategy, which ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Revisiting correlation-based functional connectivity and its relationship with structural connectivity

Dimitri Nestor Alice Van De Ville, Ali H. Sayed, Augusto José Rabelo Almeida Santos, Raphaël Pierre Liégeois

Patterns of brain structural connectivity (SC) and functional connectivity (FC) are known to be related. In SC-FC comparisons, FC has classically been evaluated from correlations between functional time series, and more recently from partial correlations o ...

MIT PRESS2020

Graph Learning Under Partial Observability

Ali H. Sayed, Augusto José Rabelo Almeida Santos

Many optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Graph Learning with Partial Observations: Role of Degree Concentration

Ali H. Sayed, Augusto José Rabelo Almeida Santos

In this work we consider the problem of learning an Erdos-Renyi graph over a diffusion network when: i) data from only a limited subset of nodes are available (partial observation); ii) and the inferential goal is to discover the graph of interconnections ...

IEEE2019

Exponential Collapse Of Social Beliefs Over Weakly-Connected Heterogeneous Networks

Ali H. Sayed, Augusto José Rabelo Almeida Santos

We consider a distributed social learning problem where a network of agents is interested in selecting one among a finite number of hypotheses. The data collected by the agents might be heterogeneous, meaning that different sub-networks might observe data ...

IEEE2019

Tomography of Large Adaptive Networks under the Dense Latent Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This work examines the problem of graph learning over a diffusion network when measurements can only be gathered from a limited fraction of agents (latent regime). Under this selling, most works in the literature rely on a degree of sparsity to provide gua ...

IEEE2018

Consistent Tomography over Diffusion Networks under the Low-Observability Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This work considers a diffusion network responding to streaming data, and studies the problem of identifying the topology of a subnetwork of observable agents by tracking their output measurements. Topology inference from indirect and/or incomplete dataset ...

IEEE2018