23 papers · ranked by Valyu relevance
Sheng Xiang, Chenhao Xu, Dawei Cheng, Xiaoyang Wang + 1 more
—Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving graphs (i.e., temporal graphs). While most of the existing graph generators focus on static graphs, the…
William Cappelletti, Étienne Voutaz, Pascal Frossard
Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evolution which arise in most real-world applications, such as social networks or physical systems. We address this gap by formally defining regimes as periods of coherent…
Sun, Qingyun, Luo, Jiayi + 10 more
—Graph neural networks have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs. However, existing GNNs exhibit poor generalization ability under distribution shifts, which is inevitable in dynamic scenarios. As dynamic graph generation progresses amid evolving latent…
José Antonio Carrillo, Antonio Esposito, László Mikolás
We focus on evolution equations on co-evolving infinite graphs and establish a rigorous link with a class of nonlinear continuity equations, whose vector fields depend on the graphs considered. More precisely, weak solutions of the so-called graph-continuity equation are shown to be the push-forward of their initial…
Justin Downes
Growing synthetic networks that follow power law distributions of a node's degree often involves adding one node at a time. Each node is added to the network with a fixed amount of edges and those edges are frozen for all future time steps. Yet real world networks often continuously evolve with edges being added and…
Gowthami Vusirikkayala, V. Madhu Viswanatham
Dynamic community detection is an increasingly important research topic in network science. In real-world networks, edges and nodes change over time; therefore, the community structure must evolve. Due to the dynamic nature of networks, many community detection algorithms rely on static graph assumptions and may not…
Lei Zhao, Hong Zhao, Mengjie Li, Jia Wang + 2 more
Introduction With the deep integration of generation-transmission-load-storage systems, the power demand side has become highly dynamic and stochastic, challenging the traditional assumption that user behavior remains stationary over time. Static clustering models therefore suffer from sensitivity to daily noise and…
Randolph Wiredu-Aidoo
—Clustering algorithms often rely on restrictive assumptions: K-Means and Gaussian Mixtures presuppose convex, Gaussian-like clusters, while DBSCAN and HDBSCAN capture non-convexity but can be highly sensitive. I introduce EVINGCA (Evolving Variance-Informed Nonparametric Graph Construction Algorithm), a…
Yucai Jiang, Rongying Shan, Gang Fu, Zhuolin Li + 4 more
Graph neural networks (GNNs), which learn node representations via aggregating their neighbors, have shown superior performance and become the de facto efficient toolkit for analyzing and learning from data with structured properties. However, most existing GNNs are designed for static graphs and assume fixed graph…
Yanfei Ma, Daozheng Qu, Yibo Wang
Misinformation proliferates on social media platforms owing to both static and dynamic user populations, where the set of active users and their interactions evolve over time. The development, amalgamation, or disintegration of communities throughout an information cascade complicates the longitudinal tracking of these…
Alejandro Feged-Rivadeneira, Felipe González-Casabianca, Pablo Cárdenas Ramírez, Andrés Ángel + 1 more
Evolutionary biology has long theorized processes—recombination, lineage divergence, drug-resistance sweeps, introgression, refugial persistence—whose signatures in genomic data are incompatible with tree structure. We argue that the shape of genetic-distance data, formalized through simplicial complexes and quantified…
Authors not listed
We present a new method for fingerprint- ing atomic configurations relevant to ML-IAM training and application, utilizing the ChIMES descriptor. These fingerprints enable rigor- ous analysis of statistical distinguishability be- tween configurations. Sample applications in- clude assessing diversity within ML-IAP…
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
Samuel Martin, Niels Holtgrefe, Vincent Moulton, Richard M Leggett + 1 more
A core goal of phylogenomics is to determine the evolutionary history of a set of species from biological sequence data. Phylogenetic networks are able to describe more complex evolutionary phenomena than phylogenetic trees but are more difficult to accurately reconstruct. Recently, there has been growing interest in…
Authors not listed
We present graphRC, a graph-based method for rapid transition state (TS) mode analysis that provides chemical insight along normal mode displacements and reaction coordinate trajectories by translating Cartesian displacements into meaningful internal coordinate changes. Internal coordinates are constructed using…
David A. Brewster, Gabor Lippner, Josef Tkadlec, Martin A. Nowak
In stochastic evolutionary dynamics, the replacement of an existing genotype or cultural trait by a newly introduced mutant is typically characterized by the quantities of fixation probability and fixation time. But in a structured population, the disappearance of a lineage occurs at a specific place. For evolutionary…
Aedan Brown, Sarah Datta, Pankaj Mehta, Brian Cleary
Population genetics has been successful in explaining how drift, selection, and mutation shape allele frequencies. However, we still lack an understanding of how biological and evolutionary constraints arising from genotype-phenotype maps shape evolutionary dynamics. To explore this question, we developed a new…
Daniel Lazarev, Anna Sappington, Grant Chau, Ruochi Zhang + 1 more
A foundational question in computational biology is how to utilize data to describe the forces driving evolution. Here, we view evolution as a novel diffusion process constrained by many biological, physical, and environmental factors affecting organism viability at any given time. We introduce DiffEvol, a framework…
Donghui Xu, Haoyuan Wu, Yonghua Wu
The process of evolutionary change remains poorly understood. By analyzing genomic data from 12 populations in Lenski’s long-term evolution experiment (LTEE) over 60,000 generations, we identified a clear sequence in gene adaptation: growth-related genes evolved early, while survival-related genes evolved later.…
Mauricio González-Forero
Mathematically integrating genetics, development, and evolution is a longstanding challenge. Here I develop general mathematical theory that integrates sexual, discrete, multilocus genetics, development, and evolution. This yields an exact method to describe the evolutionary dynamics of allele frequencies and linkage…
Authors not listed
High-entropy layered double hydroxides (HE-LDHs) have shown great potential in oxygen evolution reaction (OER) catalysis due to their tunable compositions and electronic structures. However, the synergistic effects between multiple vacancies, such as metal and oxygen vacancies, remain poorly understood and challenging…
Authors not listed
The GENERIC framework provides a robust structure for nonequilibrium dynamics but lacks a principled method to select reversible ($L$) and irreversible ($M$) brackets. Similarly, finite-time optimizations minimizing path-averaged reciprocal temperature exist but remain isolated. Here, we introduce the \textbf{Entropy…
Authors not listed
RNA droplets assembled from co-transcriptionally folded nanostructures have recently emerged as a promising platform for constructing protocells and minimal synthetic cell models. In these systems, a custom-designed DNA template encodes an RNA nanostar, which is produced by transcription and self-assembles into…