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Selected Topics in Signal Processing, IEEE Journal of

Issue 4 • Date Aug. 2014

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Displaying Results 1 - 25 of 27
  • [Front cover]

    Page(s): C1
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  • IEEE Journal of Selected Topics in Signal Processing publication information

    Page(s): C2
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  • Table of contents

    Page(s): 509 - 510
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  • Introduction to the Issue on Signal Processing for Social Networks [Guest editorial]

    Page(s): 511 - 513
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  • Multi-Layer Graph Analysis for Dynamic Social Networks

    Page(s): 514 - 523
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2069 KB) |  | HTML iconHTML  

    Modern social networks frequently encompass multiple distinct types of connectivity information; for instance, explicitly acknowledged friend relationships might complement behavioral measures that link users according to their actions or interests. One way to represent these networks is as multi-layer graphs, where each layer contains a unique set of edges over the same underlying vertices (users). Edges in different layers typically have related but distinct semantics; depending on the application multiple layers might be used to reduce noise through averaging, to perform multifaceted analyses, or a combination of the two. However, it is not obvious how to extend standard graph analysis techniques to the multi-layer setting in a flexible way. In this paper we develop latent variable models and methods for mining multi-layer networks for connectivity patterns based on noisy data. View full abstract»

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  • Graphical Evolutionary Game for Information Diffusion Over Social Networks

    Page(s): 524 - 536
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    Social networks have become ubiquitous in our daily life, as such they have attracted great research interests recently. A key challenge is that it is of extremely large-scale with tremendous information flow, creating the phenomenon of “Big Data.” Under such a circumstance, understanding information diffusion over social networks has become an important research issue. Most of the existing works on information diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the information diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored in existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we analyze the framework in uniform degree and non-uniform degree networks and derive the closed-form expressions of the evolutionary stable network states. Moreover, the information diffusion over two special networks, Erdös-Rényi random network and the Barabási-Albert scale-free network, are also highlighted. To verify our theoretical analysis, we conduct experiments by using both synthetic networks and real-world Facebook network, as well as real-world information spreading dataset of Memetracker. Experiments shows that the proposed game theoretic framework is effective and practical in modeling the social network users' information forwarding behaviors. View full abstract»

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  • Diffusion in Social Networks as SIS Epidemics: Beyond Full Mixing and Complete Graphs

    Page(s): 537 - 551
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    Peer influence and interactions between agents in a population give rise to complex, nonlinear behaviors. This paper adopts the SIS (susceptible-infected-susceptible) framework from epidemiology to analytically study how network topology affects the diffusion of ideas/opinions/beliefs/innovations in social networks. We introduce the scaled SIS process, which models peer influence as neighbor-to-neighbor infections. We model the scaled SIS process as a continuous-time Markov process and derive for this process its closed form equilibrium distribution. The adjacency matrix that describes the underlying social network is explicitly reflected in this distribution. The paper shows that interesting population asymptotic behaviors occur for scenarios where the individual tendencies of each agent oppose peer influences. Specifically, we determine how the most probable configuration of agent states (i.e., the population configuration with maximum equilibrium distribution) depends on both model parameters and network topology. We show that, for certain regions of the parameter space, this and related issues reduce to standard graph questions like the maximum independent set problem. View full abstract»

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  • Dynamic Stochastic Blockmodels for Time-Evolving Social Networks

    Page(s): 552 - 562
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1980 KB) |  | HTML iconHTML  

    Significant efforts have gone into the development of statistical models for analyzing data in the form of networks, such as social networks. Most existing work has focused on modeling static networks, which represent either a single time snapshot or an aggregate view over time. There has been recent interest in statistical modeling of dynamic networks, which are observed at multiple points in time and offer a richer representation of many complex phenomena. In this paper, we present a state-space model for dynamic networks that extends the well-known stochastic blockmodel for static networks to the dynamic setting. We fit the model in a near-optimal manner using an extended Kalman filter (EKF) augmented with a local search. We demonstrate that the EKF-based algorithm performs competitively with a state-of-the-art algorithm based on Markov chain Monte Carlo sampling but is significantly less computationally demanding. View full abstract»

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  • Proximal-Gradient Algorithms for Tracking Cascades Over Social Networks

    Page(s): 563 - 575
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    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 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 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 political leadership in North Korea and the initial public offering of LinkedIn explain connectivity changes observed in the associated networks inferred from global cascades of online media. View full abstract»

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  • Bounded Confidence Opinion Dynamics in a Social Network of Bayesian Decision Makers

    Page(s): 576 - 585
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    Bounded confidence opinion dynamics model the propagation of information in social networks. However, in the existing literature, opinions are only viewed as abstract quantities without semantics rather than as part of a decision-making system. In this work, opinion dynamics are examined when agents are Bayesian decision makers that perform hypothesis testing or signal detection, and the dynamics are applied to prior probabilities of hypotheses. Bounded confidence is defined on prior probabilities through Bayes risk error divergence, the appropriate measure between priors in hypothesis testing. This definition contrasts with the measure used between opinions in standard models: absolute error. It is shown that the rapid convergence of prior probabilities to a small number of limiting values is similar to that seen in the standard Krause-Hegselmann model. The most interesting finding in this work is that the number of these limiting values and the time to convergence changes with the signal-to-noise ratio in the detection task. The number of final values or clusters is maximal at intermediate signal-to-noise ratios, suggesting that the most contentious issues lead to the largest number of factions. It is at these same intermediate signal-to-noise ratios at which the degradation in detection performance of the aggregate vote of the decision makers is greatest in comparison to the Bayes optimal detection performance. Real-world data from the United States Senate is examined in connection with the proposed model. View full abstract»

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  • How to Identify an Infection Source With Limited Observations

    Page(s): 586 - 597
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2688 KB) |  | HTML iconHTML  

    A rumor spreading in a social network or a disease propagating in a community can be modeled as an infection spreading in a network. Finding the infection source is a challenging problem, which is made more difficult in many applications where we have access only to a limited set of observations. We consider the problem of estimating an infection source for a Susceptible-Infected model, in which not all infected nodes can be observed. When the network is a tree, we show that an estimator for the source node associated with the most likely infection path that yields the limited observations is given by a Jordan center, i.e., a node with minimum distance to the set of observed infected nodes. We also propose approximate source estimators for general networks. Simulation results on various synthetic networks and real world networks suggest that our estimators perform better than distance, closeness, and betweenness centrality based heuristics . View full abstract»

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  • Convergence Analysis of Iterated Belief Revision in Complex Fusion Environments

    Page(s): 598 - 612
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    We study convergence of iterated belief revision in complex fusion environments, which may consist of a network of soft (i.e., human or human-based) and hard (i.e., conventional physics-based) sensors and where agent communications may be asynchronous and the link structure may be dynamic. In particular, we study the problem in which network agents exchange and revise belief functions (which generalize probability mass functions) and are more geared towards handling the uncertainty pervasive in soft/hard fusion environments. We focus on belief revision in which agents utilize a generalized fusion rule that is capable of generating a rational consensus. It includes the widely used weighted average consensus as a special case. By establishing this fusion scheme as a pool of paracontracting operators, we derive general convergence criteria that are relevant for a wide range of applications. Furthermore, we analyze the conditions for consensus for various social networks by simulating several network topologies and communication patterns that are characteristic of such networks. View full abstract»

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  • Graph-Theoretic Distributed Inference in Social Networks

    Page(s): 613 - 623
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    We consider distributed inference in social networks where a phenomenon of interest evolves over a given social interaction graph, referred to as the social digraph. We assume that a network of agents monitors certain nodes in the social digraph and the agents rely on inter-agent communication to perform inference. The key contributions include: (i) a novel construction of the distributed estimator and distributed observability from the first principles; (ii) a graph-theoretic agent classification that establishes the importance and role of each agent towards inference; (iii) characterizing the necessary conditions, based on the classification in (ii), on the agent network to achieve distributed observability. Our results are based on structured systems theory and are applicable to any parameter choice of the underlying system matrix as long as the social digraph remains fixed. In other words, any social phenomena that evolves (linearly) over a structure-invariant social digraph may be considered-we refer to such systems as Liner Structure-Invariant (LSI). The aforementioned contributions, (i)-(iii), thus, only require the knowledge of the social digraph (topology) and are independent of the social phenomena. We show the applicability of the results to several real-wold social networks, i.e. social influence among monks, networks of political blogs and books, and a co-authorship graph. View full abstract»

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  • Provenance-Assisted Classification in Social Networks

    Page(s): 624 - 637
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3577 KB) |  | HTML iconHTML  

    Signal feature extraction and classification are two common tasks in the signal processing literature. This paper investigates the use of source identities as a common mechanism for enhancing the classification accuracy of social signals. We define social signals as outputs, such as microblog entries, geotags, or uploaded images, contributed by users in a social network. Many classification tasks can be defined on such outputs. For example, one may want to identify the dialect of a microblog contributed by an author, or classify information referred to in a user's tweet as true or false. While the design of such classifiers is application-specific, social signals share in common one key property: they are augmented by the explicit identity of the source. This motivates investigating whether or not knowing the source of each signal (in addition to exploiting signal features) allows the classification accuracy to be improved. We call it provenance-assisted classification. This paper answers the above question affirmatively, demonstrating how source identities can improve classification accuracy, and derives confidence bounds to quantify the accuracy of results. Evaluation is performed in two real-world contexts: (i) fact-finding that classifies microblog entries into true and false, and (ii) language classification of tweets issued by a set of possibly multi-lingual speakers. We also carry out extensive simulation experiments to further evaluate the performance of the proposed classification scheme over different problem dimensions. The results show that provenance features significantly improve classification accuracy of social signals, even when no information is known about the sources (besides their ID). This observation offers a general mechanism for enhancing classification results in social networks. View full abstract»

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  • Distributed Online Learning in Social Recommender Systems

    Page(s): 638 - 652
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    In this paper, we consider decentralized sequential decision making in distributed online recommender systems, where items are recommended to users based on their search query as well as their specific background including history of bought items, gender and age, all of which comprise the context information of the user. In contrast to centralized recommender systems, in which there is a single centralized seller who has access to the complete inventory of items as well as the complete record of sales and user information, in decentralized recommender systems each seller/learner only has access to the inventory of items and user information for its own products and not the products and user information of other sellers, but can get commission if it sells an item of another seller. Therefore, the sellers must distributedly find out for an incoming user which items to recommend (from the set of own items or items of another seller), in order to maximize the revenue from own sales and commissions. We formulate this problem as a cooperative contextual bandit problem, analytically bound the performance of the sellers compared to the best recommendation strategy given the complete realization of user arrivals and the inventory of items, as well as the context-dependent purchase probabilities of each item, and verify our results via numerical examples on a distributed data set adapted based on Amazon data. We evaluate the dependence of the performance of a seller on the inventory of items the seller has, the number of connections it has with the other sellers, and the commissions which the seller gets by selling items of other sellers to its users. View full abstract»

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  • Quickest Attack Detection in Multi-Agent Reputation Systems

    Page(s): 653 - 666
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2412 KB) |  | HTML iconHTML  

    This paper considers the security issue of the reputation systems, which make great use of social networks, to form the reputation scores of a certain participant in the network by collecting ratings from others. To prevent the damage caused by the intentional injection of dishonest ratings, termed as reputation attack, we aim to detect such attack as soon as possible after its occurrence by sequentially observing the rating samples at a single or multiple agents. We propose a discrete probability model to characterize the rating samples based on which the sequential change detection framework can be applied. First for the single-agent case, we develop a sequential attack detector based on the generalized likelihood ratio (GLR) that is robust to a wide range of attacking strategies. Then, for the multi-agent case, where the distributed agents collect ratings and communicate with a central manager to make the decision, we propose a novel multi-agent sequential attack detector that can effectively exploit the different number of attacked agents to increase the detection speed, and exhibits a second-order asymptotic optimality for any given number of attacked agents if the rating distribution under attack can be specified. Finally, we propose a decentralized version of the multi-agent attack detector based on the level-triggered sampling of the local statistic at each agent, which essentially constitutes an adaptive transmission scheme between the distributed agents and the central manager. The decentralized detector incurs only a minor increase in detection delay compared with the centralized counterpart while substantially reduces the communication overhead for attack detection in the multi-agent reputation systems. View full abstract»

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  • Reliable Crowdsourcing for Multi-Class Labeling Using Coding Theory

    Page(s): 667 - 679
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    Crowdsourcing systems often have crowd workers that perform unreliable work on the task they are assigned. In this paper, we propose the use of error-control codes and decoding algorithms to design crowdsourcing systems for reliable classification despite unreliable crowd workers. Coding theory based techniques also allow us to pose easy-to-answer binary questions to the crowd workers. We consider three different crowdsourcing models: systems with independent crowd workers, systems with peer-dependent reward schemes, and systems where workers have common sources of information. For each of these models, we analyze classification performance with the proposed coding-based scheme. We develop an ordering principle for the quality of crowds and describe how system performance changes with the quality of the crowd. We also show that pairing among workers and diversification of the questions help in improving system performance. We demonstrate the effectiveness of the proposed coding-based scheme using both simulated data and real datasets from Amazon Mechanical Turk, a crowdsourcing microtask platform. Results suggest that use of good codes may improve the performance of the crowdsourcing task over typical majority-voting approaches. View full abstract»

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  • Social Collaborative Retrieval

    Page(s): 680 - 689
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    Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many potential applications that involve aspects of both recommendation and information retrieval, and the task of collaborative retrieval-a combination of these two traditional problems-has recently been introduced. Successful collaborative retrieval requires overcoming severe data sparsity, making additional sources of information, such as social graphs, particularly valuable. In this paper we propose a new model for collaborative retrieval, and show that our algorithm outperforms current state-of-the-art approaches by incorporating information from social networks. We also provide empirical analyses of the ways in which cultural interests propagate along a social graph using a real-world music dataset. View full abstract»

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  • Optimal Subsidies for Shared Small Cell Networks —A Social Network Perspective

    Page(s): 690 - 702
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    In this paper, we study the optimal differentiated subsidies from a mobile network operator (MNO) to form a shared small cell network using social network information. It is a win-win strategy for the MNO to offload data and for small cell users (SUs) to receive enhanced QoS. We formulate the problem as a Stackelberg Game: in Stage I, the MNO sets differentiated subsidies and maximizes a subsidy elasticity of sharing metric, which captures the goal of using the least subsidies to obtain the most shared network; in Stage II, each SU decides the degree of sharing by maximizing a utility function, which is a tradeoff between proportional-fair capacity and subsidy. In our model, at subgame perfect equilibrium, the shared network can be maintained with the termination of subsidies. Our results can be summarized as follows. Firstly, we propose a deterministic framework where each SU's type information is known to the MNO and show that SUs' sharing strategies only depend on their type information. The subsidies act as intermediate variables, which will not affect the intrinsic structure of the shared network. The sensitivity analyses and the robust counterpart are investigated in terms of capacity perturbations and mobility uncertainties, respectively. Furthermore, we propose a dynamic framework by assuming that the mobility of each SU is an independent lazy random walk. We show that our proposed framework converges to the optimal solution at a geometric rate. Utilizing SU's type information, our work provides a framework on how to formulate a stable shared network with a unique equilibrium via subsidies as intermediate helpers. View full abstract»

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  • Asynchronous Gossip for Averaging and Spectral Ranking

    Page(s): 703 - 716
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    We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis. View full abstract»

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  • Sharing in Networks of Strategic Agents

    Page(s): 717 - 731
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    In social, economic and engineering networks, connected agents need to cooperate by repeatedly sharing information and/or goods. Typically, sharing is costly and there are no immediate benefits for agents who share. Hence, agents who strategically aim to maximize their own individual utilities will “free-ride” because they lack incentives to cooperate/share, thereby leading to inefficient operation or even collapse of networks. To incentivize the strategic agents to cooperate with each other, we design distributed rating protocols which exploit the ongoing nature of the agents' interactions to assign ratings and through them, determine future rewards and punishments: agents that have behaved as directed enjoy high ratings-and hence greater future access to the information/goods of others; agents that have not behaved as directed enjoy low ratings-and hence less future access to the information/goods of others. Unlike existing rating protocols, the proposed protocol operates in a distributed manner and takes into consideration the underlying interconnectivity of agents as well as their heterogeneity. We prove that in many networks, the price of anarchy (PoA) obtained by adopting the proposed rating protocols is 1, that is, the optimal social welfare is attained. In networks where PoA is larger than 1, we show that the proposed rating protocol significantly outperforms existing incentive mechanisms. Last but not least, the proposed rating protocols can also operate efficiently in dynamic networks, where new agents enter the network over time. View full abstract»

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  • IEEE Journal of Selected Topics in Signal Processing information for authors

    Page(s): 732 - 733
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  • Special issue on advances in hyperspectral data processing and analysis

    Page(s): 734
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  • Open Access

    Page(s): 735
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  • IEEE xplore digital library

    Page(s): 736
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Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Fernando Pereira
Instituto Superior Técnico