Brian Baingana | University of Minnesota - Twin Cities (original) (raw)

Papers by Brian Baingana

2016 50th Asilomar Conference on Signals, Systems and Computers, 2016

Directed networks are pervasive both in nature and engineered systems, often underlying the compl... more Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their explicit structures are often unobservable, in order to facilitate network analytics, one generally resorts to approaches capitalizing on measurable nodal processes to infer the unknown topology. Prominent among these are structural equation models (SEMs), capable of incorporating exogenous inputs to resolve inherent directional ambiguities. However, this assumes full knowledge of exogenous inputs, which may not be readily available in some practical settings. The present paper advocates a novel SEM-based topology inference approach that entails a PARAFAC decomposition of a three-way tensor, constructed from the observed nodal data. It turns out that second-order statistics of exogenous variables suffice to identify the hidden topology. Leveraging the uniqueness properties inherent to high-order tensor factorizations, it is shown that topology identification is possible under reasonably mild conditions. Tests on simulated data corroborate the effectiveness of the novel tensor-based approach.

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016

Real-world networks are known to exhibit community structure, characterized by presence of dense ... more Real-world networks are known to exhibit community structure, characterized by presence of dense node clusters with loose edge connections among them. Although identification of communities is a well-studied subject, most approaches only focus on edge-based criteria which may not incorporate important grouping information captured by higher-order structures e.g., cliques and cycles, to name a few. In order to overcome this limitation, the present paper advocates a novel three-way tensor network representation that captures spatial dependencies among node neighborhoods. Each tensor slice captures a connectivity matrix pertaining to a unique egonet, defined as the subgraph induced by a node and its single-hop neighbors. Constrained tensor factorization is pursued to reveal the hidden and possibly overlapping community structure. Numerical tests on synthetic and real world networks corroborate the efficacy of the novel approach.

10.1.1 Signal Processing for Big Data.......................... 374

Many real-world processes evolve in cascades over complex networks, whose topologies are often un... more Many real-world processes evolve in cascades over complex networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when blogs mention popular news items, individuals in a community catch an infectious disease, or consumers adopt a trendy electronics product are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a dynamic structural equation model is adopted to capture the relationship between observed adoption times and the unknown edge weights. Assuming a slowly time-varying topology and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentiallyweighted least-squares criterion. To this end, solvers with complementary strengths are developed by leveraging (pseudo) real-time sparsity-promoting proximal gradient iterations, the improved convergence rate of accelerated variants, or reduced computational complexity of stochastic gradient descent. Numerical tests with both synthetic and real data demonstrate the effectiveness of the novel algorithms in unveiling sparse dynamically-evolving topologies, while accounting for external influences in the adoption times. Key events in the recent succession of political leadership in North Korea, explain connectivity changes observed in the associated network inferred from global cascades of online media.

Motivation • Cascading processes such as web events, infectious diseases, product adoption propag... more Motivation • Cascading processes such as web events, infectious diseases, product adoption propagate over implicit networks [Easley10].

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016

This paper deals with tracking dynamic piecewise-constant network topologies that underpin comple... more This paper deals with tracking dynamic piecewise-constant network topologies that underpin complex systems including online social networks, neural pathways in the brain, and the world-wide web. Leveraging a structural equation model (SEM) in which only second-order statistics of exogenous inputs are known, the topology inference problem is recast using three-way tensors constructed from observed nodal data. To facilitate real-time operation, an adaptive parallel factor (PARAFAC) tensor decomposition is advocated to track the topology-revealing tensor factors. Preliminary tests on simulated data corroborate the effectiveness of the novel tensor-based approach.

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017

Linear structural vector autoregressive models constitute a generalization of structural equation... more Linear structural vector autoregressive models constitute a generalization of structural equation models (SEMs) and vector autoregressive (VAR) models, two popular approaches for topology inference of directed graphs. Although simple and tractable, linear SVARMs seldom capture nonlinearities that are inherent to complex systems, such as the human brain. To this end, the present paper advocates kernel-based nonlinear SVARMs, and develops an efficient sparsity-promoting least-squares estimator to learn the hidden topology. Numerical tests on real electrocorticographic (ECoG) data from an Epilepsy study corroborate the efficacy of the novel approach.

IEEE Transactions on Signal Processing, 2019

Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families... more Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families of approaches that have been shown useful in effective brain connectivity studies. While VARMs postulate that a given region of interest in the brain is directionally connected to another one by virtue of time-lagged influences, SEMs assert that directed dependencies arise due to instantaneous effects, and may even be adopted when nodal measurements are not necessarily multivariate time series. To unify these complementary perspectives, linear structural vector autoregressive models (SVARMs) that leverage both instantaneous and time-lagged nodal data have recently been put forth. Albeit simple and tractable, linear SVARMs are quite limited since they are incapable of modeling nonlinear dependencies between neuronal time series. To this end, the overarching goal of the present paper is to considerably broaden the span of linear SVARMs by capturing nonlinearities through kernels, which have recently emerged as a powerful nonlinear modeling framework in canonical machine learning tasks, e.g., regression, classification, and dimensionality reduction. The merits of kernel-based methods are extended here to the task of learning the effective brain connectivity, and an efficient regularized estimator is put forth to leverage the edge sparsity inherent to real-world complex networks. Judicious kernel choice from a preselected dictionary of kernels is also addressed using a data-driven approach. Numerical tests on ECoG data captured through a study on epileptic seizures demonstrate that it is possible to unveil previously unknown directed links between brain regions of interest.

IEEE Transactions on Signal Processing, 2017

Directed networks are pervasive both in nature and engineered systems, often underlying the compl... more Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their structures are often unobservable, in order to facilitate network analytics, one generally resorts to approaches capitalizing on measurable nodal processes to infer the unknown topology. Structural equation models (SEMs) are capable of incorporating exogenous inputs to resolve inherent directional ambiguities. However, conventional SEMs assume full knowledge of exogenous inputs, which may not be readily available in some practical settings. The present paper advocates a novel SEM-based topology inference approach that entails factorization of a three-way tensor, constructed from the observed nodal data, using the wellknown parallel factor (PARAFAC) decomposition. It turns out that second-order piecewise stationary statistics of exogenous variables suffice to identify the hidden topology. Capitalizing on the uniqueness properties inherent to high-order tensor factorizations, it is shown that topology identification is possible under reasonably mild conditions. In addition, to facilitate real-time operation and inference of time-varying networks, an adaptive (PARAFAC) tensor decomposition scheme which tracks the topology-revealing tensor factors is developed. Extensive tests on simulated and real stock quote data demonstrate the merits of the novel tensor-based approach.

IEEE Transactions on Signal Processing, 2017

Contagions such as the spread of popular news stories, or infectious diseases, propagate in casca... more Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies. However, "social signals" such as product purchase time, or blog entry timestamps are measurable, and implicitly depend on the underlying topology, making it possible to track it over time. Interestingly, network topologies often "jump" between discrete states that may account for sudden changes in the observed signals. The present paper advocates a switched dynamic structural equation model to capture the topology-dependent cascade evolution, as well as the discrete states driving the underlying topologies. Conditions under which the proposed switched model is identifiable are established. Leveraging the edge sparsity inherent to social networks, a recursive 1-norm regularized least-squares estimator is put forth to jointly track the states and network topologies. An efficient first-order proximal-gradient algorithm is developed to solve the resulting optimization problem. Numerical experiments on both synthetic data and real cascades measured over the span of one year are conducted, and test results corroborate the efficacy of the advocated approach.

IEEE Transactions on Signal Processing, 2017

Structural equation models (SEMs) have been widely adopted for inference of causal interactions i... more Structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or rumors propagate. The appeal of SEMs in these settings stems from their simplicity and tractability, since they typically assume linear dependencies among observable variables. Acknowledging the limitations inherent to adopting linear models, the present paper puts forth nonlinear SEMs, which account for (possible) nonlinear dependencies among network nodes. The advocated approach leverages kernels as a powerful encompassing framework for nonlinear modeling, and an efficient estimator with affordable tradeoffs is put forth. Interestingly, pursuit of the novel kernel-based approach yields a convex regularized estimator that promotes edge sparsity, a property exhibited by most real-world networks, and the resulting optimization problem is amenable to proximal-splitting optimization methods. To this end, solvers with complementary merits are developed by leveraging the alternating direction method of multipliers, and proximal gradient iterations. Experiments conducted on simulated data demonstrate that the novel approach outperforms linear SEMs with respect to edge detection errors. Furthermore, tests on a real gene expression dataset unveil interesting new edges that were not revealed by linear SEMs, which could shed more light on regulatory behavior of human genes.

2016 Annual Conference on Information Science and Systems (CISS), 2016

Linear structural equation models (SEMs) have been widely adopted for inference of causal interac... more Linear structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or rumors propagation. However, these approaches are limited because they assume linear dependence among observable variables. The present paper advocates a more general nonlinear structural equation model based on polynomial expansions, which compensates for possible nonlinear dependencies between network nodes. To this end, a group-sparsity regularized estimator is put forth to leverage the inherent edge sparsity that is present in most real-world networks. A novel computationally-efficient proximal gradient algorithm is developed to estimate the polynomial SEM coefficients, and hence infer the edge structure. Preliminary tests on simulated data demonstrate the effectiveness of the novel approach.

2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015

Network communities exist as clusters of nodes whose intra-edge connectivity is stronger than edg... more Network communities exist as clusters of nodes whose intra-edge connectivity is stronger than edge connectivities between nodes from different clusters. Among others, identification of hidden communities unveils shared functional roles in biological networks, and assigns individuals in social networks to consumer groups for more targeted advertising. This is a rather challenging task in large-size networks due to temporal evolution of the underlying topology, presence of distortive anomalies, as well as the sheer scale of network data, often stored in distributed clusters. Most contemporary approaches resort to batch centralized processing, and are ill-equipped to address the afore-mentioned challenges. The present paper develops a novel decentralized algorithm for tracking overlapping communities in dynamic networks, while compensating for distortions due to anomalous nodes. Experiments conducted on global trade flow data demonstrate the efficacy of the proposed approach.

IEEE Transactions on Signal Processing, 2016

2015 49th Asilomar Conference on Signals, Systems and Computers, 2015

Although the kriged Kalman filter (KKF) has welldocumented merits for prediction of spatial-tempo... more Although the kriged Kalman filter (KKF) has welldocumented merits for prediction of spatial-temporal processes, its performance degrades in the presence of outliers due to anomalous events, or measurement equipment failures. This paper proposes a robust KKF model that explicitly accounts for presence of measurement outliers. Exploiting outlier sparsity, a novel 1-regularized estimator that jointly predicts the spatialtemporal process at unmonitored locations, while identifying measurement outliers is put forth. Numerical tests are conducted on a synthetic Internet protocol (IP) network, and real transformer load data. Test results corroborate the effectiveness of the novel estimator in joint spatial prediction and outlier identification.

2013 IEEE Global Conference on Signal and Information Processing, 2013

ABSTRACT

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015

Many real-world processes evolve in cascades over complex networks, whose topologies are often un... more Many real-world processes evolve in cascades over complex networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when blogs mention popular news items, individuals in a community catch an infectious disease, or consumers adopt a trendy electronics product are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a \textit{dynamic} structural equation model is adopted to capture the relationship between observed adoption times and the unknown edge weights. Assuming a slowly time-varying topology and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentially-weighted least-squares criterion. To this end, solvers with complementary strengths are developed by leveraging (pseudo) real-time sparsity-promoting proximal gradient iterations, the improved convergence rate of accelerated variants, or reduced ...

2016 50th Asilomar Conference on Signals, Systems and Computers, 2016

Directed networks are pervasive both in nature and engineered systems, often underlying the compl... more Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their explicit structures are often unobservable, in order to facilitate network analytics, one generally resorts to approaches capitalizing on measurable nodal processes to infer the unknown topology. Prominent among these are structural equation models (SEMs), capable of incorporating exogenous inputs to resolve inherent directional ambiguities. However, this assumes full knowledge of exogenous inputs, which may not be readily available in some practical settings. The present paper advocates a novel SEM-based topology inference approach that entails a PARAFAC decomposition of a three-way tensor, constructed from the observed nodal data. It turns out that second-order statistics of exogenous variables suffice to identify the hidden topology. Leveraging the uniqueness properties inherent to high-order tensor factorizations, it is shown that topology identification is possible under reasonably mild conditions. Tests on simulated data corroborate the effectiveness of the novel tensor-based approach.

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016

Real-world networks are known to exhibit community structure, characterized by presence of dense ... more Real-world networks are known to exhibit community structure, characterized by presence of dense node clusters with loose edge connections among them. Although identification of communities is a well-studied subject, most approaches only focus on edge-based criteria which may not incorporate important grouping information captured by higher-order structures e.g., cliques and cycles, to name a few. In order to overcome this limitation, the present paper advocates a novel three-way tensor network representation that captures spatial dependencies among node neighborhoods. Each tensor slice captures a connectivity matrix pertaining to a unique egonet, defined as the subgraph induced by a node and its single-hop neighbors. Constrained tensor factorization is pursued to reveal the hidden and possibly overlapping community structure. Numerical tests on synthetic and real world networks corroborate the efficacy of the novel approach.

10.1.1 Signal Processing for Big Data.......................... 374

Many real-world processes evolve in cascades over complex networks, whose topologies are often un... more Many real-world processes evolve in cascades over complex networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when blogs mention popular news items, individuals in a community catch an infectious disease, or consumers adopt a trendy electronics product are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a dynamic structural equation model is adopted to capture the relationship between observed adoption times and the unknown edge weights. Assuming a slowly time-varying topology and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentiallyweighted least-squares criterion. To this end, solvers with complementary strengths are developed by leveraging (pseudo) real-time sparsity-promoting proximal gradient iterations, the improved convergence rate of accelerated variants, or reduced computational complexity of stochastic gradient descent. Numerical tests with both synthetic and real data demonstrate the effectiveness of the novel algorithms in unveiling sparse dynamically-evolving topologies, while accounting for external influences in the adoption times. Key events in the recent succession of political leadership in North Korea, explain connectivity changes observed in the associated network inferred from global cascades of online media.

Motivation • Cascading processes such as web events, infectious diseases, product adoption propag... more Motivation • Cascading processes such as web events, infectious diseases, product adoption propagate over implicit networks [Easley10].

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016

This paper deals with tracking dynamic piecewise-constant network topologies that underpin comple... more This paper deals with tracking dynamic piecewise-constant network topologies that underpin complex systems including online social networks, neural pathways in the brain, and the world-wide web. Leveraging a structural equation model (SEM) in which only second-order statistics of exogenous inputs are known, the topology inference problem is recast using three-way tensors constructed from observed nodal data. To facilitate real-time operation, an adaptive parallel factor (PARAFAC) tensor decomposition is advocated to track the topology-revealing tensor factors. Preliminary tests on simulated data corroborate the effectiveness of the novel tensor-based approach.

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017

Linear structural vector autoregressive models constitute a generalization of structural equation... more Linear structural vector autoregressive models constitute a generalization of structural equation models (SEMs) and vector autoregressive (VAR) models, two popular approaches for topology inference of directed graphs. Although simple and tractable, linear SVARMs seldom capture nonlinearities that are inherent to complex systems, such as the human brain. To this end, the present paper advocates kernel-based nonlinear SVARMs, and develops an efficient sparsity-promoting least-squares estimator to learn the hidden topology. Numerical tests on real electrocorticographic (ECoG) data from an Epilepsy study corroborate the efficacy of the novel approach.

IEEE Transactions on Signal Processing, 2019

Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families... more Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families of approaches that have been shown useful in effective brain connectivity studies. While VARMs postulate that a given region of interest in the brain is directionally connected to another one by virtue of time-lagged influences, SEMs assert that directed dependencies arise due to instantaneous effects, and may even be adopted when nodal measurements are not necessarily multivariate time series. To unify these complementary perspectives, linear structural vector autoregressive models (SVARMs) that leverage both instantaneous and time-lagged nodal data have recently been put forth. Albeit simple and tractable, linear SVARMs are quite limited since they are incapable of modeling nonlinear dependencies between neuronal time series. To this end, the overarching goal of the present paper is to considerably broaden the span of linear SVARMs by capturing nonlinearities through kernels, which have recently emerged as a powerful nonlinear modeling framework in canonical machine learning tasks, e.g., regression, classification, and dimensionality reduction. The merits of kernel-based methods are extended here to the task of learning the effective brain connectivity, and an efficient regularized estimator is put forth to leverage the edge sparsity inherent to real-world complex networks. Judicious kernel choice from a preselected dictionary of kernels is also addressed using a data-driven approach. Numerical tests on ECoG data captured through a study on epileptic seizures demonstrate that it is possible to unveil previously unknown directed links between brain regions of interest.

IEEE Transactions on Signal Processing, 2017

Directed networks are pervasive both in nature and engineered systems, often underlying the compl... more Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their structures are often unobservable, in order to facilitate network analytics, one generally resorts to approaches capitalizing on measurable nodal processes to infer the unknown topology. Structural equation models (SEMs) are capable of incorporating exogenous inputs to resolve inherent directional ambiguities. However, conventional SEMs assume full knowledge of exogenous inputs, which may not be readily available in some practical settings. The present paper advocates a novel SEM-based topology inference approach that entails factorization of a three-way tensor, constructed from the observed nodal data, using the wellknown parallel factor (PARAFAC) decomposition. It turns out that second-order piecewise stationary statistics of exogenous variables suffice to identify the hidden topology. Capitalizing on the uniqueness properties inherent to high-order tensor factorizations, it is shown that topology identification is possible under reasonably mild conditions. In addition, to facilitate real-time operation and inference of time-varying networks, an adaptive (PARAFAC) tensor decomposition scheme which tracks the topology-revealing tensor factors is developed. Extensive tests on simulated and real stock quote data demonstrate the merits of the novel tensor-based approach.

IEEE Transactions on Signal Processing, 2017

Contagions such as the spread of popular news stories, or infectious diseases, propagate in casca... more Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies. However, "social signals" such as product purchase time, or blog entry timestamps are measurable, and implicitly depend on the underlying topology, making it possible to track it over time. Interestingly, network topologies often "jump" between discrete states that may account for sudden changes in the observed signals. The present paper advocates a switched dynamic structural equation model to capture the topology-dependent cascade evolution, as well as the discrete states driving the underlying topologies. Conditions under which the proposed switched model is identifiable are established. Leveraging the edge sparsity inherent to social networks, a recursive 1-norm regularized least-squares estimator is put forth to jointly track the states and network topologies. An efficient first-order proximal-gradient algorithm is developed to solve the resulting optimization problem. Numerical experiments on both synthetic data and real cascades measured over the span of one year are conducted, and test results corroborate the efficacy of the advocated approach.

IEEE Transactions on Signal Processing, 2017

Structural equation models (SEMs) have been widely adopted for inference of causal interactions i... more Structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or rumors propagate. The appeal of SEMs in these settings stems from their simplicity and tractability, since they typically assume linear dependencies among observable variables. Acknowledging the limitations inherent to adopting linear models, the present paper puts forth nonlinear SEMs, which account for (possible) nonlinear dependencies among network nodes. The advocated approach leverages kernels as a powerful encompassing framework for nonlinear modeling, and an efficient estimator with affordable tradeoffs is put forth. Interestingly, pursuit of the novel kernel-based approach yields a convex regularized estimator that promotes edge sparsity, a property exhibited by most real-world networks, and the resulting optimization problem is amenable to proximal-splitting optimization methods. To this end, solvers with complementary merits are developed by leveraging the alternating direction method of multipliers, and proximal gradient iterations. Experiments conducted on simulated data demonstrate that the novel approach outperforms linear SEMs with respect to edge detection errors. Furthermore, tests on a real gene expression dataset unveil interesting new edges that were not revealed by linear SEMs, which could shed more light on regulatory behavior of human genes.

2016 Annual Conference on Information Science and Systems (CISS), 2016

Linear structural equation models (SEMs) have been widely adopted for inference of causal interac... more Linear structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or rumors propagation. However, these approaches are limited because they assume linear dependence among observable variables. The present paper advocates a more general nonlinear structural equation model based on polynomial expansions, which compensates for possible nonlinear dependencies between network nodes. To this end, a group-sparsity regularized estimator is put forth to leverage the inherent edge sparsity that is present in most real-world networks. A novel computationally-efficient proximal gradient algorithm is developed to estimate the polynomial SEM coefficients, and hence infer the edge structure. Preliminary tests on simulated data demonstrate the effectiveness of the novel approach.

2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015

Network communities exist as clusters of nodes whose intra-edge connectivity is stronger than edg... more Network communities exist as clusters of nodes whose intra-edge connectivity is stronger than edge connectivities between nodes from different clusters. Among others, identification of hidden communities unveils shared functional roles in biological networks, and assigns individuals in social networks to consumer groups for more targeted advertising. This is a rather challenging task in large-size networks due to temporal evolution of the underlying topology, presence of distortive anomalies, as well as the sheer scale of network data, often stored in distributed clusters. Most contemporary approaches resort to batch centralized processing, and are ill-equipped to address the afore-mentioned challenges. The present paper develops a novel decentralized algorithm for tracking overlapping communities in dynamic networks, while compensating for distortions due to anomalous nodes. Experiments conducted on global trade flow data demonstrate the efficacy of the proposed approach.

IEEE Transactions on Signal Processing, 2016

2015 49th Asilomar Conference on Signals, Systems and Computers, 2015

Although the kriged Kalman filter (KKF) has welldocumented merits for prediction of spatial-tempo... more Although the kriged Kalman filter (KKF) has welldocumented merits for prediction of spatial-temporal processes, its performance degrades in the presence of outliers due to anomalous events, or measurement equipment failures. This paper proposes a robust KKF model that explicitly accounts for presence of measurement outliers. Exploiting outlier sparsity, a novel 1-regularized estimator that jointly predicts the spatialtemporal process at unmonitored locations, while identifying measurement outliers is put forth. Numerical tests are conducted on a synthetic Internet protocol (IP) network, and real transformer load data. Test results corroborate the effectiveness of the novel estimator in joint spatial prediction and outlier identification.

2013 IEEE Global Conference on Signal and Information Processing, 2013

ABSTRACT

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015

Many real-world processes evolve in cascades over complex networks, whose topologies are often un... more Many real-world processes evolve in cascades over complex networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when blogs mention popular news items, individuals in a community catch an infectious disease, or consumers adopt a trendy electronics product are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a \textit{dynamic} structural equation model is adopted to capture the relationship between observed adoption times and the unknown edge weights. Assuming a slowly time-varying topology and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentially-weighted least-squares criterion. To this end, solvers with complementary strengths are developed by leveraging (pseudo) real-time sparsity-promoting proximal gradient iterations, the improved convergence rate of accelerated variants, or reduced ...