Selection of Information Streams in Social Sensing: an Interdependence- and Cost-aware Ranking Method

In this work we address the problem of critical source selection in social sensing. We propose an approach to the ranking of information streams, which is aware of the interdependence among streams (redundancy and synergies), of the cost of individual streams, and of the cost related to the integration of multiple streams. The method is based on the use of the Coalitional Game Theory concept of Power Index, and relies on the polynomial-time estimate of the stream sets characteristics. With respect to other works using a power index, the method takes into account that the problem has a non-trivial cost structure.

[1]  Eytan Ruppin,et al.  Feature Selection Based on the Shapley Value , 2005, IJCAI.

[2]  Paul Marks Crowds point out potholes on a map to speed up street repairs , 2013 .

[3]  Yves Crama,et al.  Boolean methods in operations research and related areas , 2011 .

[4]  Martin Shubik,et al.  A Method for Evaluating the Distribution of Power in a Committee System , 1954, American Political Science Review.

[5]  Chao Huang,et al.  Critical Source Selection in Social Sensing Applications , 2017, 2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS).

[6]  Divesh Srivastava,et al.  Characterizing and selecting fresh data sources , 2014, SIGMOD Conference.

[7]  Charu C. Aggarwal,et al.  Managing and Mining Sensor Data , 2013, Springer US.

[8]  Eytan Ruppin,et al.  Feature Selection via Coalitional Game Theory , 2007, Neural Computation.

[9]  Dong Wang,et al.  CovidSens: a vision on reliable social sensing for COVID-19 , 2020, Artificial Intelligence Review.

[10]  Charu C. Aggarwal,et al.  Data Extrapolation in Social Sensing for Disaster Response , 2014, 2014 IEEE International Conference on Distributed Computing in Sensor Systems.

[11]  Lionel Brunie,et al.  Managing a pool of rules for credit card fraud detection by a Game Theory based approach , 2020, Future Gener. Comput. Syst..

[12]  Talal Rahwan,et al.  Using the Shapley Value to Analyze Algorithm Portfolios , 2016, AAAI.

[13]  Heng Ji,et al.  The Age of Social Sensing , 2018, Computer.

[14]  Chao Huang,et al.  Topic-Aware Social Sensing with Arbitrary Source Dependency Graphs , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[15]  Salil S. Kanhere,et al.  Trust-based privacy-aware participant selection in social participatory sensing , 2015, J. Inf. Secur. Appl..

[16]  Charu C. Aggarwal,et al.  Using humans as sensors: An estimation-theoretic perspective , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[17]  M. Grabisch,et al.  Transversality of the Shapley value , 2008 .

[18]  Lloyd S. Shapley,et al.  Additive and non-additive set functions , 1953 .

[19]  Md. Yusuf Sarwar Uddin,et al.  On diversifying source selection in social sensing , 2012, 2012 Ninth International Conference on Networked Sensing (INSS).

[20]  Gabriele Gianini,et al.  Selecting Feature-Words in Tag Sense Disambiguation Based on Their Shapley Value , 2016, 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[21]  S. Lipovetsky,et al.  Analysis of regression in game theory approach , 2001 .

[22]  Ernesto Damiani,et al.  Analysis of Shapelet Transform Usage in Traffic Event Detection , 2018, 2018 IEEE International Conference on Cognitive Computing (ICCC).

[23]  Michael Granitzer,et al.  Analysing Neural Network Topologies: a Game Theoretic Approach , 2018, KES.

[24]  Divesh Srivastava,et al.  Less is More: Selecting Sources Wisely for Integration , 2012, Proc. VLDB Endow..

[25]  Divesh Srivastava,et al.  Fusing data with correlations , 2014, SIGMOD Conference.

[26]  Philippe Smets,et al.  The Transferable Belief Model for Quantified Belief Representation , 1998 .

[27]  Peter L. Hammer,et al.  Approximations of pseudo-Boolean functions; applications to game theory , 1992, ZOR Methods Model. Oper. Res..

[28]  Oskar Skibski,et al.  Axiomatic Characterization of Game-Theoretic Centrality , 2018, J. Artif. Intell. Res..

[29]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[30]  E. Kalai,et al.  On weighted Shapley values , 1983 .

[31]  E. Leher An axiomatization of the Banzhaf value , 1988 .

[32]  Di Wang,et al.  Framework for traffic event detection using Shapelet Transform , 2019, Eng. Appl. Artif. Intell..

[33]  Michel Grabisch,et al.  Set Functions, Games and Capacities in Decision Making , 2016 .