More Like this

1. Ranking on Network of Heterogeneous Information Networks

   https://doi.org/10.1109/BigData50022.2020.9378121  

   Xu, Zhe; Zhang, Si; Xia, Yinglong; Xiong, Liang; Tong, Hanghang (December 2020, IEEE BigData)

   null (Ed.)

   Ranking on networks plays an important role in many high-impact applications, including recommender systems, social network analysis, bioinformatics and many more. In the age of big data, a recent trend is to address the variety aspect of network ranking. Among others, two representative lines of research include (1) heterogeneous information network with different types of nodes and edges, and (2) network of networks with edges at different resolutions. In this paper, we propose a new network model named Network of Heterogeneous Information Networks (NeoHIN for short) that is capable of simultaneously modeling both different types of nodes/edges, and different edge resolutions. We further propose two new ranking algorithms on NeoHIN based on the cross-domain consistency principle. Experiments on synthetic and real-world networks show that our proposed algorithms are (1) effective, which outperform other existing methods, and (2) efficient, without additional time cost per iteration to their counterparts. 

   more » « less
2. Aggression rates increase around seasonally exploited resources in a primarily grass-eating primate

   https://doi.org/10.1093/beheco/arad079  

   Jarvey, Julie_C; Low, Bobbi_S; Azanaw_Haile, Abebaw; Chiou, Kenneth_L; Snyder-Mackler, Noah; Lu, Amy; Bergman, Thore_J; Beehner, Jacinta_C; Schneider-Crease, India_A; le_Roux, ed., Aliza (October 2023, Behavioral Ecology)

   Abstract Female social relationships are often shaped by the distribution of dietary resources. Socioecological models predict that females should form strict linear dominance hierarchies when resources are clumped and exhibit more egalitarian social structures when resources are evenly distributed. While many frugivores and omnivores indeed exhibit dominance hierarchies accompanied by differential resource access, many folivores deviate from the expected pattern and display dominance hierarchies despite evenly distributed resources. Among these outliers, geladas (Theropithecus gelada) present a conspicuous puzzle; females exhibit aggressive competition and strict dominance hierarchies despite feeding primarily on non-monopolizable grasses. However, these grasses become scarce in the dry season and geladas supplement their diet with underground storage organs that require relatively extensive energy to extract. We tested whether female dominance hierarchies provide differential access to underground storage organs by assessing how rank, season, and feeding context affect aggression in geladas under long-term study in the Simien Mountains National Park, Ethiopia. We found that the likelihood of receiving aggression was highest when feeding belowground and that the inverse relationship between rank and aggression was the most extreme while feeding belowground in the dry season. These results suggest that aggression in geladas revolves around belowground foods, which may mean that underground storage organs are an energetically central dietary component (despite being consumed less frequently than grasses), or that even “fallback” foods can influence feeding competition and social relationships. Further work should assess whether aggression in this context is directly associated with high-ranking usurpation of belowground foods from lower-ranking females following extraction. 

   more » « less
3. Inferring longitudinal hierarchies: Framework and methods for studying the dynamics of dominance

   https://doi.org/10.1111/1365-2656.12951  

   Strauss, Eli D.; Holekamp, Kay E.; Jackson, ed., Andrew (February 2019, Journal of Animal Ecology)

   Abstract Social inequality is a consistent feature of animal societies, often manifesting as dominance hierarchies, in which each individual is characterized by a dominance rank denoting its place in the network of competitive relationships among group members. Most studies treat dominance hierarchies as static entities despite their true longitudinal, and sometimes highly dynamic, nature.To guide study of the dynamics of dominance, we propose the concept of a longitudinal hierarchy: the characterization of a single, latent hierarchy and its dynamics over time. Longitudinal hierarchies describe the hierarchy position (r) and dynamics (∆) associated with each individual as a property of its interaction data, the periods into which these data are divided based on a period delineation rule (p) and the method chosen to infer the hierarchy. Hierarchy dynamics result from both active (∆a) and passive (∆p) processes. Methods that infer longitudinal hierarchies should optimize accuracy of rank dynamics as well as of the rank orders themselves, but no studies have yet evaluated the accuracy with which different methods infer hierarchy dynamics.We modify three popular ranking approaches to make them better suited for inferring longitudinal hierarchies. Our three “informed” methods assign ranks that are informed by data from the prior period rather than calculating ranksde novoin each observation period and use prior knowledge of dominance correlates to inform placement of new individuals in the hierarchy. These methods are provided in an R package.Using both a simulated dataset and a long‐term empirical dataset from a species with two distinct sex‐based dominance structures, we compare the performance of these methods and their unmodified counterparts. We show that choice of method has dramatic impacts on inference of hierarchy dynamics via differences in estimates of∆a. Methods that calculate ranksde novoin each period overestimate hierarchy dynamics, but incorporation of prior information leads to more accurately inferred∆a. Of the modified methods, Informed MatReorder infers the most conservative estimates of hierarchy dynamics and Informed Elo infers the most dynamic hierarchies.This work provides crucially needed conceptual framing and methodological validation for studying social dominance and its dynamics. 

   more » « less
4. A unified statistical learning model for rankings and scores with application to grant panel review

   Pearce, Michael; Erosheva, Elena A. (July 2022, Journal of machine learning research)

   Rankings and scores are two common data types used by judges to express preferences and/or perceptions of quality in a collection of objects. Numerous models exist to study data of each type separately, but no unified statistical model captures both data types simultaneously without first performing data conversion. We propose the Mallows-Binomial model to close this gap, which combines a Mallows $$\phi$$ ranking model with Binomial score models through shared parameters that quantify object quality, a consensus ranking, and the level of consensus among judges. We propose an efficient tree-search algorithm to calculate the exact MLE of model parameters, study statistical properties of the model both analytically and through simulation, and apply our model to real data from an instance of grant panel review that collected both scores and partial rankings. Furthermore, we demonstrate how model outputs can be used to rank objects with confidence. The proposed model is shown to sensibly combine information from both scores and rankings to quantify object quality and measure consensus with appropriate levels of statistical uncertainty. 

   more » « less
5. How Does Past Performance of Competitors Influence Designers’ Cognition, Behaviors, and Outcomes?

   https://doi.org/10.1115/1.4054604  

   Shergadwala, Murtuza N.; Panchal, Jitesh H.; Bilionis, Ilias (October 2022, Journal of Mechanical Design)

   Abstract Existing literature on information sharing in contests has established that sharing contest-specific information influences contestant behaviors, and thereby, the outcomes of a contest. However, in the context of engineering design contests, there is a gap in knowledge about how contest-specific information such as competitors’ historical performance influences designers’ actions and the resulting design outcomes. To address this gap, the objective of this study is to quantify the influence of information about competitors’ past performance on designers’ belief about the outcomes of a contest, which influences their design decisions, and the resulting design outcomes. We focus on a single-stage design competition where an objective figure of merit is available to the contestants for assessing the performance of their design. Our approach includes (i) developing a behavioral model of sequential decision making that accounts for information about competitors’ historical performance and (ii) using the model in conjunction with a human-subject experiment where participants make design decisions given controlled strong or weak performance records of past competitors. Our results indicate that participants spend greater efforts when they know that the contest history reflects that past competitors had a strong performance record than when it reflects a weak performance record. Moreover, we quantify cognitive underpinnings of such informational influence via our model parameters. Based on the parametric inferences about participants’ cognition, we suggest that contest designers are better off not providing historical performance records if past contest outcomes do not match their expectations setup for a given design contest. 

   more » « less