We present a physics-inspired method for inferring dynamic rankings in directed temporal networks-networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction. The inferred ranking of each node is real-valued and varies in time as each new edge, encoding an outcome like a win or loss, raises or lowers the node's estimated strength or prestige, as is often observed in real scenarios including sequences of games, tournaments, or interactions in animal hierarchies. Our method works by solving a linear system of equations and requires only one parameter to be tuned. As a result, the corresponding algorithm is scalable and efficient. We test our method by evaluating its ability to predict interactions (edges' existence) and their outcomes (edges' directions) in a variety of applications, including both synthetic and real data. Our analysis shows that in many cases our method's performance is better than existing methods for predicting dynamic rankings and interaction outcomes.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevE.110.034310 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Machine learning and metagenomics identifies uncharacterized taxa inferred to drive biogeochemical cycles in a subtropical hypereutrophic estuary.
ISME Commun
January 2024
School of Chemistry and Molecular Biosciences, Australian Centre for Ecogenomics, The University of Queensland, QLD 4072, Australia.
Anthropogenic influences have drastically increased nutrient concentrations in many estuaries globally, and microbial communities have adapted to the resulting hypereutrophic ecosystems. However, our knowledge of the dominant microbial taxa and their potential functions in these ecosystems has remained sparse. Here, we study prokaryotic community dynamics in a temporal-spatial dataset, from a subtropical hypereutrophic estuary.
View Article and Find Full Text PDFSubgraph Topology and Dynamic Graph Topology Enhanced Graph Learning and Pairwise Feature Context Relationship Integration for Predicting Disease-Related miRNAs.
J Chem Inf Model
January 2025
School of Cyberspace Security, Hainan University, Haikou 570228, China.
As an increasing number of microRNAs (miRNAs) have become biomarkers of various human diseases, prediction of the candidate disease-related miRNAs is helpful for facilitating the early diagnosis of diseases. Most of the recent prediction models concentrated on learning of the features from the heterogeneous graph composed of miRNAs and diseases. However, they failed to fully exploit the subgraph structures consisting of multiple miRNA and disease nodes, and they also did not completely integrate the context relationships among the pairwise features.
View Article and Find Full Text PDFHuman organotypic colon in vitro microtissue: unveiling a new window into colonic drug disposition.
Eur J Pharm Sci
January 2025
Preclinical Sciences & Translational Safety, Janssen R&D, Turnhoutseweg 30, 2340, Beerse, Belgium. Electronic address:
The purpose of this study was to evaluate EpiColon, a novel human organotypic 3D colon microtissue prototype, developed to assess colonic drug disposition, with a particular focus on permeability ranking, and compare its performance to Caco-2 monolayers. EpiColon was characterized for barrier function using transepithelial electrical resistance (TEER), morphology via histology and immunohistochemistry, and functionality through drug transport studies measuring apparent permeability (P). Cutoff thresholds for the permeability of FITC-dextran 4 kDa (FD4), FITC-dextran 10 kDa (FD10S), and [C]mannitol were established to monitor microtissue integrity.
View Article and Find Full Text PDFIn silico analysis of novel Triacontafluoropentadec-1-ene as a sustainable replacement for dodecane in fisheries microplastics: Molecular docking, dynamics simulation and pharmacophore studies of acetylcholinesterase activity.
Comput Biol Chem
January 2025
Department of Biostatistics and Bioinformatics, ICMR-National Institute for Research in Environmental Health, Bhopal, Madhya Pradesh, India. Electronic address:
Plastics play an essential role in modern fisheries and their degradation releases micro- and nano-sized plastic particles which further causes ecological and human health hazards through various environmental contamination pathways and toxicity mechanisms, which can cause respiratory problems, cancer, reproductive toxicity, endocrine disruption and neurological effects in humans. This study utilized various bioinformatics tools through multi-step computational analyses to investigate the interactions between prevalent fisheries microplastics and the key protein receptor acetylcholinesterase (AChE), which is associated with neurotoxicity, as it can interfere with nerve impulses and muscle control. Our results indicate that the binding of seven polymers within AChE's active site, with dodecane and polypropylene exhibited highest affinity with hydrogen bonding were observed through Molecular docking of different program (PyRx) and servers (CB-Dock, eDock) then the stability of AChE-dodecane and AChE-polypropylene complexes were observed through MD simulations for 100 ns.
View Article and Find Full Text PDFCross-Domain Person Re-Identification Based on Multi-Branch Pose-Guided Occlusion Generation.
Sensors (Basel)
January 2025
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Aiming at the problems caused by a lack of feature matching due to occlusion and fixed model parameters in cross-domain person re-identification, a method based on multi-branch pose-guided occlusion generation is proposed. This method can effectively improve the accuracy of person matching and enable identity matching even when pedestrian features are misaligned. Firstly, a novel pose-guided occlusion generation module is designed to enhance the model's ability to extract discriminative features from non-occluded areas.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!