Introducing drivers' collaboration network: A two-layers social network perspective in road transportation system analysis

Abstract Drivers can make significant impacts on transportation systems. They can leave important information due to their social behaviors. But, the role of drivers has been overlooked yet. In this paper, for the first time, drivers' collaboration network is introduced. The network is considered in a heterogenous form, because of existence multiple relationships between drivers in the real-world situation. Since drivers do not belong to only one community, the overlapping of communities is considered and a new overlapping community detection algorithm is developed to discover the hidden structure of the network. Also, we present a new overlapping score to improve the community detection algorithm, using the adjacencies among non-memeber neighbor nodes and communities. Solving the algorithm will lead to discovering dense communities of drivers that have meaningful relationships with each other. This will result in a better understanding of the transportation network and also improving the overall performance of the system. A comparison of the developed algorithm with the others demonstrates the effectiveness of the algorithm. To evaluate the applicability of the algorithm, a real drivers' collaboration network is presented and the developed algorithm is applied to derive insights.

[1]  Rushed Kanawati,et al.  YASCA: An Ensemble-Based Approach for Community Detection in Complex Networks , 2014, COCOON.

[2]  Hai Yang,et al.  Solving a discrete multimodal transportation network design problem , 2014 .

[3]  Rushed Kanawati,et al.  Community detection in multiplex networks: A seed-centric approach , 2015, Networks Heterog. Media.

[4]  Alireza Bagheri,et al.  Community detection in facebook activity networks and presenting a new multilayer label propagation algorithm for community detection , 2019, International Journal of Modern Physics B.

[5]  M. Figueroa,et al.  Improving urban freight governance and stakeholder management: A social systems approach combined with relationship platforms and value co-creation , 2017 .

[6]  Clara Pizzuti,et al.  Community Detection in Multidimensional Networks , 2014, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence.

[7]  Santo Fortunato,et al.  Fast consensus clustering in complex networks , 2019, Physical review. E.

[8]  Jiwon Kim,et al.  Identification of communities in urban mobility networks using multi-layer graphs of network traffic , 2017 .

[9]  Carla E. Brodley,et al.  Solving cluster ensemble problems by bipartite graph partitioning , 2004, ICML.

[10]  Roselina Sallehuddin,et al.  Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends , 2020, Telecommunication Systems.

[11]  Francesco Calabrese,et al.  ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS , 2013, Data Mining and Knowledge Discovery.

[12]  Rushed Kanawati,et al.  A multiplex-network based approach for clustering ensemble selection , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[13]  Rushed Kanawati,et al.  LICOD: A Leader-driven algorithm for community detection in complex networks , 2014, Vietnam Journal of Computer Science.

[14]  Bin Wu,et al.  A link clustering based overlapping community detection algorithm , 2013, Data Knowl. Eng..

[15]  Andrea Tagarelli,et al.  Ensemble-based community detection in multilayer networks , 2017, Data Mining and Knowledge Discovery.

[16]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Fosca Giannotti,et al.  Finding and Characterizing Communities in Multidimensional Networks , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[18]  Zili Zhang,et al.  A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting , 2016, Neurocomputing.

[19]  A. Badiee,et al.  A monopoly pricing model for diffusion maximization based on heterogeneous nodes and negative network externalities (Case study: A novel product) , 2018 .

[20]  R. Rothenberg,et al.  Risk network structure in the early epidemic phase of HIV transmission in Colorado Springs , 2002, Sexually transmitted infections.

[21]  S. Strogatz,et al.  Redrawing the Map of Great Britain from a Network of Human Interactions , 2010, PloS one.

[22]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[23]  Henrik Jeldtoft Jensen,et al.  Comparison of Communities Detection Algorithms for Multiplex , 2014, ArXiv.

[24]  Isaac Olusegun Osunmakinde,et al.  Temporality in link prediction , 2009 .

[25]  Isaac Olusegun Osunmakinde,et al.  Temporality in Link Prediction: Understanding Social Complexity , 2009 .

[26]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[27]  Huiru Zheng,et al.  Flow Simulation on Multilayer Networks: a New Approach to Community Detection in Complex Systems , 2018, 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

[28]  Yu Cui,et al.  Travel Behavior Classification: An Approach with Social Network and Deep Learning , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[29]  Huan Liu,et al.  Community Detection and Mining in Social Media , 2010, Community Detection and Mining in Social Media.

[30]  Suzanna Long,et al.  The role of stakeholder engagement in the development of sustainable rail infrastructure systems , 2013 .

[31]  Andrea Tagarelli,et al.  Consensus Community Detection in Multilayer Networks using Parameter-free Graph Pruning , 2018, PAKDD.

[32]  Feng Luo,et al.  Exploring Local Community Structures in Large Networks , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[33]  Michael Batty,et al.  Detecting the dynamics of urban structure through spatial network analysis , 2014, Int. J. Geogr. Inf. Sci..

[34]  Nam P. Nguyen,et al.  Overlapping Community Structures and Their Detection on Social Networks , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[35]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[36]  Jukka-Pekka Onnela,et al.  Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.

[37]  Babak Amiri,et al.  A hybrid artificial immune network for detecting communities in complex networks , 2014, Computing.

[38]  Mehdi Ghazanfari,et al.  Development of a monopoly pricing model for diffusion maximization in fuzzy weighted social networks with negative externalities of heterogeneous nodes using a case study , 2019, Neural Computing and Applications.

[39]  Yong Zhou,et al.  Overlapping Community Detection by Local Community Expansion , 2015, J. Inf. Sci. Eng..

[40]  Maoguo Gong,et al.  Detecting composite communities in multiplex networks: A multilevel memetic algorithm , 2017, Swarm Evol. Comput..

[41]  Márton Karsai,et al.  User-based representation of time-resolved multimodal public transportation networks , 2015, Royal Society Open Science.

[42]  Mostafa Salehi,et al.  Multilayer overlapping community detection using multi-objective optimization , 2019, Future Gener. Comput. Syst..

[43]  Clara Pizzuti,et al.  Many-objective optimization for community detection in multi-layer networks , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[44]  Rushed Kanawati,et al.  Multiplex Network Mining: A Brief Survey , 2015, IEEE Intell. Informatics Bull..

[45]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[46]  Raj Rao Nadakuditi,et al.  Spectra of random graphs with arbitrary expected degrees , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[47]  Monika Bansal,et al.  Ranking and Discovering Anomalous Neighborhoods in Attributed Multiplex Networks , 2020, COMAD/CODS.

[48]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[49]  Dane Taylor,et al.  Enhanced detectability of community structure in multilayer networks through layer aggregation , 2015, Physical review letters.

[50]  Daniel D. Suthers,et al.  Discovery of Community Structures in a Heterogeneous Professional Online Network , 2013, 2013 46th Hawaii International Conference on System Sciences.