Cyber-Social Systems: Modeling, Inference, and Optimal Design

This paper models the cyber-social system as a cyber-network of agents monitoring states of individuals in a social network. The state of each individual is represented by a social node, and the interactions among individuals are represented by a social link. In the cyber-network, each node represents an agent, and the links represent information sharing among agents. The agents make an observation of social states and perform distributed inference. In this direction, the contribution of this paper is threefold: First, a novel distributed inference protocol is proposed that makes no assumption on the rank of the underlying social system. This is significant as most protocols in the literature only work on full-rank systems. Second, a novel agent classification is developed, where it is shown that the connectivity requirement of the cyber-network differs for each type. This is particularly important in finding the minimal number of observations and minimal connectivity of the cyber-network as the next contribution. Third, the cost-optimal design of the cyber-network constraint with distributed observability is addressed. This problem is subdivided into sensing cost optimization and networking cost optimization, where both are claimed to be NP-hard. We solve both the problems for certain types of social networks and find polynomial-order solutions.

[1]  Gregory Gutin,et al.  Digraphs - theory, algorithms and applications , 2002 .

[2]  Soummya Kar,et al.  Distributed Consensus Algorithms in Sensor Networks With Imperfect Communication: Link Failures and Channel Noise , 2007, IEEE Transactions on Signal Processing.

[3]  Anna Scaglione,et al.  Active Sensing of Social Networks , 2016, IEEE Transactions on Signal and Information Processing over Networks.

[4]  Usman A. Khan,et al.  Distributed Estimation Recovery Under Sensor Failure , 2017, IEEE Signal Processing Letters.

[5]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[6]  Anna Scaglione,et al.  Estimating Social Opinion Dynamics Models From Voting Records , 2018, IEEE Transactions on Signal Processing.

[7]  G. Battistelli,et al.  An Information-Theoretic Approach to Distributed State Estimation , 2011 .

[8]  Soummya Kar,et al.  Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication , 2008, IEEE Transactions on Information Theory.

[9]  Robert E. Tarjan,et al.  Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..

[10]  John N. Tsitsiklis,et al.  On Krause's Multi-Agent Consensus Model With State-Dependent Connectivity , 2008, IEEE Transactions on Automatic Control.

[11]  BoydStephen,et al.  Distributed average consensus with least-mean-square deviation , 2007 .

[12]  R. May,et al.  Stability and Complexity in Model Ecosystems , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[14]  Albert-László Barabási,et al.  Observability of complex systems , 2013, Proceedings of the National Academy of Sciences.

[15]  Joseph JáJá,et al.  Approximation Algorithms for Several Graph Augmentation Problems , 1981, SIAM J. Comput..

[16]  Christian Commault,et al.  Generic properties and control of linear structured systems: a survey , 2003, Autom..

[17]  Ali H. Sayed,et al.  Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis , 2008, IEEE Transactions on Signal Processing.

[18]  Mehran Mesbahi,et al.  On strong structural controllability of networked systems: A constrained matching approach , 2013, 2013 American Control Conference.

[19]  Soummya Kar,et al.  Structurally Observable Distributed Networks of Agents Under Cost and Robustness Constraints , 2017, IEEE Transactions on Signal and Information Processing over Networks.

[20]  Prasanna Chaporkar,et al.  Minimum Cost Feedback Selection for Arbitrary Pole Placement in Structured Systems , 2017, IEEE Transactions on Automatic Control.

[21]  Siddhartha Kumar Khaitan,et al.  Design Techniques and Applications of Cyberphysical Systems: A Survey , 2015, IEEE Systems Journal.

[22]  George J. Pappas,et al.  Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks , 2015, IEEE Control Systems.

[23]  David W. Pentico,et al.  Assignment problems: A golden anniversary survey , 2007, Eur. J. Oper. Res..

[24]  E. Xing,et al.  A state-space mixed membership blockmodel for dynamic network tomography , 2008, 0901.0135.

[25]  Dominique Sauter,et al.  Structural Analysis of the Partial State and Input Observability for Structured Linear Systems: Application to Distributed Systems , 2009, Eur. J. Control.

[26]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[27]  Fei-Yue Wang,et al.  The Emergence of Intelligent Enterprises: From CPS to CPSS , 2010, IEEE Intelligent Systems.

[28]  Robert E. Tarjan,et al.  Efficient algorithms for finding minimum spanning trees in undirected and directed graphs , 1986, Comb..

[29]  Runliang Dou,et al.  Optimizing Sensor Network Coverage and Regional Connectivity in Industrial IoT Systems , 2017, IEEE Systems Journal.

[30]  Usman A. Khan,et al.  Structural Cost-Optimal Design of Sensor Networks for Distributed Estimation , 2018, IEEE Signal Processing Letters.

[31]  Usman A. Khan,et al.  Graph-Theoretic Distributed Inference in Social Networks , 2014, IEEE Journal of Selected Topics in Signal Processing.

[32]  J. French A formal theory of social power. , 1956, Psychology Review.

[33]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[34]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[35]  Stephen P. Boyd,et al.  Distributed average consensus with least-mean-square deviation , 2007, J. Parallel Distributed Comput..

[36]  Athanasios V. Vasilakos,et al.  Cyber physical systems technologies and applications , 2016, Future Gener. Comput. Syst..

[37]  Usman A. Khan,et al.  Measurement partitioning and observational equivalence in state estimation , 2014, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[38]  Soummya Kar,et al.  Minimum number of information gatherers to ensure full observability of a dynamic social network: A structural systems approach , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[39]  M. Hou Discussion on: ''Structural Analysis of the Partial State and Input Observability for Structured Linear Systems. Application to Distributed Systems'' , 2009 .

[40]  Shinkyu Park,et al.  Necessary and sufficient conditions for the stabilizability of a class of LTI distributed observers , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[41]  Christian Commault,et al.  Observability Preservation Under Sensor Failure , 2008, IEEE Transactions on Automatic Control.

[42]  Richard M. Karp,et al.  Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems , 1972, Combinatorial Optimization.

[43]  Qi Hao,et al.  Multiple Human Tracking and Identification With Wireless Distributed Pyroelectric Sensor Systems , 2009, IEEE Systems Journal.

[44]  Soummya Kar,et al.  Gossip Algorithms for Distributed Signal Processing , 2010, Proceedings of the IEEE.

[45]  Usman A. Khan,et al.  Coordinated networked estimation strategies using structured systems theory , 2011, IEEE Conference on Decision and Control and European Control Conference.

[46]  R. Prim Shortest connection networks and some generalizations , 1957 .

[47]  Soummya Kar,et al.  On connectivity, observability, and stability in distributed estimation , 2010, 49th IEEE Conference on Decision and Control (CDC).

[48]  M. Degroot Reaching a Consensus , 1974 .

[49]  Usman A. Khan,et al.  Topology design in networked estimation: A generic approach , 2013, 2013 American Control Conference.

[50]  L. Ghaoui,et al.  A cone complementarity linearization algorithm for static output-feedback and related problems , 1997, IEEE Trans. Autom. Control..

[51]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[52]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[53]  Hamid R. Rabiee,et al.  Observational Equivalence in System Estimation: Contractions in Complex Networks , 2017, IEEE Transactions on Network Science and Engineering.

[54]  Usman A. Khan,et al.  On the Genericity Properties in Distributed Estimation: Topology Design and Sensor Placement , 2012, IEEE Journal of Selected Topics in Signal Processing.

[55]  Noah E. Friedkin,et al.  ATTITUDE CHANGE, AFFECT CONTROL, AND EXPECTATION STATES IN THE FORMATION OF INFLUENCE NETWORKS , 2003 .

[56]  Asuman E. Ozdaglar,et al.  Opinion Dynamics and Learning in Social Networks , 2010, Dyn. Games Appl..