@inproceedings{85a51424f7164e1ba375ef773332085e,
title = "Intrinsic Degree: An Estimator of the Local Growth Rate in Graphs",
abstract = "The neighborhood size of a query node in a graph often grows exponentially with the distance to the node, making a neighborhood search prohibitively expensive even for small distances. Estimating the growth rate of the neighborhood size is therefore an important task in order to determine an appropriate distance for which the number of traversed nodes during the search will be feasible. In this work, we present the intrinsic degree model, which captures the growth rate of exponential functions through the analysis of the infinitesimal vicinity of the origin. We further derive an estimator which allows to apply the intrinsic degree model to graphs. In particular, we can locally estimate the growth rate of the neighborhood size by observing the close neighborhood of some query points in a graph. We evaluate the performance of the estimator through experiments on both artificial and real networks.",
keywords = "Degree, Estimation, Graph, Intrinsic dimensionality",
author = "{von Ritter}, Lorenzo and Houle, {Michael E.} and Stephan G{\"u}nnemann",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 11th International Conference on Similarity Search and Applications, SISAP 2018 ; Conference date: 07-10-2018 Through 09-10-2018",
year = "2018",
doi = "10.1007/978-3-030-02224-2_15",
language = "English",
isbn = "9783030022235",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "195--208",
editor = "St{\'e}phane Marchand-Maillet and Silva, {Yasin N.} and Edgar Ch{\'a}vez",
booktitle = "Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings",
}