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Towards interactive betweenness centrality estimation for transportation network using capsule network

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dc.contributor.authorMatin, Abdul, author
dc.contributor.authorPallickara, Sangmi Lee, advisor
dc.contributor.authorPallickara, Shrideep, committee member
dc.contributor.authorBhaskar, Aditi S., committee member
dc.date.accessioned2023-01-21T01:24:07Z
dc.date.available2023-01-21T01:24:07Z
dc.date.issued2022
dc.description.abstractThe node importance of a graph needs to be estimated for many graph-based applications. One of the most popular metrics for measuring node importance is betweenness centrality, which measures the amount of influence a node has over the flow of information in a graph. However, the computation complexity of calculating betweenness centrality is extremely high with large- scale graphs. This is especially true when analyzing the road networks of states with millions of nodes and edges, making it infeasible to calculate their betweenness centrality (BC) in real- time using traditional iterative methods. The application of a machine learning model to predict the importance of nodes provides opportunities to address this issue. Graph Neural Networks (GNNs), which have been gaining popularity in recent years, are particularly well-suited for graph analysis. In this study, we propose a deep learning architecture RoadCaps to estimate the BC by merging Capsule Neural Networks with Graph Convolutional Networks (GCN), a convolution operation based GNN. We target the effective aggregation of features from neighbor nodes to approximate the correct BC of a node. We leverage patterns capturing the strength of the capsule network to effectively estimate the node level BC from the high-level information generated by the GCN block. We further compare the model accuracy and effectiveness of RoadCaps with the other two GCN-based models. We also analyze the efficiency and effectiveness of RoadCaps for different aspects like scalability and robustness. We perform one empirical benchmark with the road network for the entire state of California. The overall analysis shows that our proposed network can provide more accurate road importance estimation, which is helpful for rapid response planning such as evacuation during wildfires and flooding.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierMatin_colostate_0053N_17491.pdf
dc.identifier.urihttps://hdl.handle.net/10217/235949
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectbig data
dc.subjectevacuation route planning
dc.subjectroad importance
dc.subjectcapsule network
dc.subjectbetweeness centrality
dc.subjectGCN
dc.titleTowards interactive betweenness centrality estimation for transportation network using capsule network
dc.typeText
dc.typeImage
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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