%0 Journal Article %1 Koopmann2024 %A Koopmann, Tobias %A Becker, Martin %A Lemmerich, Florian %A Hotho, Andreas %D 2024 %J Data Mining and Knowledge Discovery %K %R 10.1007/s10618-023-00996-8 %T CompTrails: comparing hypotheses across behavioral networks %U https://doi.org/10.1007/s10618-023-00996-8 %X The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.

@article{Koopmann2024, abstract = {The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.}, added-at = {2024-01-10T17:22:11.000+0100}, author = {Koopmann, Tobias and Becker, Martin and Lemmerich, Florian and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2ddbbfdf9cfd978b0438b948b6784ccab/tobias.koopmann}, day = 03, doi = {10.1007/s10618-023-00996-8}, interhash = {f6f4a3464992e7f4c0d1935ecd9efe86}, intrahash = {ddbbfdf9cfd978b0438b948b6784ccab}, issn = {1573-756X}, journal = {Data Mining and Knowledge Discovery}, keywords = {}, month = {01}, timestamp = {2024-01-10T17:22:11.000+0100}, title = {CompTrails: comparing hypotheses across behavioral networks}, url = {https://doi.org/10.1007/s10618-023-00996-8}, year = 2024 }