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1. Neural Similarity Learning

   Weiyang Liu, Zhen Liu (January 2019, Advances in neural information processing systems)
2. Evaluating Molecular Similarity Measures: Do Similarity Measures Reflect Electronic Structure Properties?

   https://doi.org/10.1021/acs.jcim.5c00175  

   Duke, Rebekah; Yang, Chih-Hsuan; Ganapathysubramanian, Baskar; Risko, Chad (May 2025, Journal of Chemical Information and Modeling)
3. Food Web Similarity Increases With Productivity Similarity at a Continental Scale

   https://doi.org/10.1111/ele.70368  

   Finneran, Ann E; Uribe, César A; Hung, Kai M; Hsieh, Chia; Wuensch, Matthew A; McCary, Matthew A; Beaudrot, Lydia (March 2026, Ecology Letters)

   ABSTRACT Primary productivity and trophic interactions are fundamentally linked. However, it remains largely unknown how food web structure varies along primary productivity gradients at continental scales or how the influence of primary productivity on food webs varies within regions. Furthermore, anthropogenic pressure threatens the integrity of food webs globally with potentially predictable food web disassembly. Here, we test how plant productivity and anthropogenic fragmentation predict the pairwise similarity of food web networks within and among regions for 127 protected areas spanning deserts to rainforests. We measured food web structural equivalence independent of species identities and accounted for inherent scaling of food web structure with richness and connectance. Food webs were significantly more similar at sites with similar plant productivity at the continental scale and within woodland savannas, and in tropical rainforests with similar anthropogenic fragmentation. These empirical results inform how food web structure mediates biodiversity and ecosystem function. 

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4. Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity

   https://doi.org/10.1177/00187208221142126  

   Kamaraj, Amudha V.; Lee, Joonbum; Domeyer, Joshua E.; Liu, Shu-Yuan; Lee, John D. (January 2023, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   ObjectiveThis study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. BackgroundSimilarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent’s actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences. MethodsA driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity. ResultsObjective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants’ approach to the intersection and not their departure. ConclusionDeveloping driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver’s perception of similarity, and (b) understand what elements of the driving style govern subjective similarity. 

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5. Adaptive MapReduce Similarity Joins

   https://doi.org/10.1145/3206333.3206340  

   McCauley, Samuel; Silvestri, Francesco (June 2018, 5th ACM SIGMOD Workshop on Algorithms and Systems for MapReduce and Beyond)