A machine learning approach for predicting human shortest path task performance

doi:10.1016/j.visinf.2022.04.001

2022-06-01

Shijun Cai *

Seok-Hee Hong

Xiaobo Xia

Tongliang Liu (Tongliang Liu)

Weidong Huang

DOI：10.1016/j.visinf.2022.04.001

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Finding a shortest path for a given pair of vertices in a graph drawing is one of the fundamental tasks for qualitative evaluation of graph drawings. In this paper, we present the first machine learning approach to predict human shortest path task performance, including accuracy, response time, and mental effort. To predict the shortest path task performance, we utilize correlated quality metrics and the ground truth data from the shortest path experiments. Specifically, we introduce path faithfulness metrics and show strong correlations with the shortest path task performance. Moreover, to mitigate the problem of insufficient ground truth training data, we use the transfer learning method to pre-train our deep model, exploiting the correlated quality metrics. Experimental results using the ground truth human shortest path experiment data show that our models can successfully predict the shortest path task performance. In particular, model MSP achieves the MSE (i.e., test mean square error) of 0.7243 (i.e., data range from -17.27 to 1.81) for prediction.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keyword:

Graphdrawing

Machinelearning

Shortestpathtask

Qualitymetrics