出版社：The Japanese Society for Artificial Intelligence
摘要：In this paper, we propose novel centrality measures which extract important nodes from weighted networks like road networks where an actual distance is assigned over each link. Since the distances between nodes are not taken into consideration in traditional centrality measures like closeness and betweenness, there is a limit to an application to a real world problem for road networks with distances. Aiming at extracting important sightseeing spots so as to improve the convenience of tourists, we propose two measures considering actual distances, one is ``detour centrality'' which is an easiness measure of brief detour and the other is ``convenience centrality'' which is an accessibility measure based on traditional closeness and betweenness centrality respectively. Furthermore, when we extracting two or more nodes, there is a problem which these nodes are located in near places with each other only by extracting nodes with high rank of a centrality measure, since the whole balance is not taken into consideration. To overcome these shortcomings, we extend the above-mentioned centrality measures to ``set detour centrality'' and ``set convenience centrality'' and attempt to maximize the utility of all tourists over the target area by extracting set of nodes so as to maximize the values of these set centrality measures. In our experiments using two real sightseeing spot datasets, we show that our extended measures can extract an appropriate set of spots in terms of easiness of detour and accessibility, and these measures are robust to change of distances and emerging some outlier spots.
关键词：road network ; actual distance ; centrality measure ; navigation system ; sightseeing
