Articles tagged with Graph Theory
Researchers develop a new approach for reconstructing graphs with incomplete information, handling both feature and structure completion. The EWS-RGCN model uses separate channels and a multi-level contrastive graph mask autoencoder to overcome limitations of weak supervision and limited labeled nodes.
Researchers at Rice University develop a new way to model the cochlea's sound processing using graph signal processing, identifying broader functional relationships between sensory cells. The model, called GSP Cochlea, performs better in detecting signals in noise and may hold the key to personalized device settings for hearing aids.
HelixAI develops AI-driven platform for researchers and clinicians to integrate complex biomedical data, improving diagnosis and prognosis. It also launches Helix for Longevity, a consumer-facing application estimating biological age and providing personalized recommendations for health promotion.
Jundong Li, an associate professor at the University of Virginia, has received the 2025 Tao Li Award for his significant contributions to data mining and machine learning. His research focuses on developing models that can extract actionable insights from structured data, particularly graphs.
This book explores the impact of decentralized networks on industries like healthcare and supply chains, highlighting the benefits of blockchain technology. It also delves into the synergy between blockchain and emerging technologies like AI and IoT.
Researchers successfully constructed a large molecular spherical shell structure with the geometric topology of a regular dodecahedron through entanglement of peptides with metal ions. The resulting M60L60 metal-peptide shell exhibits remarkable stability against heat, dilution, and oxidative conditions, making it a promising platform ...
Thatchaphol Saranurak and Andrew Owens have been awarded Sloan Research Fellowships for their innovative work on graph networks and machine perception systems. Their research aims to create more efficient algorithms for computing dynamic systems, such as social networks and traffic patterns.
A recent study found that social networks with protected minority opinions foster innovation, economic prosperity, and collective intelligence. Decentralizing social networks by unfollowing influential individuals can promote sociodiversity.
New study uses high-powered microscopy and mathematical theory to unveil nanoscale voids in three dimensions. The findings show a strong correlation between unique physical properties of random empty space and improved filtration performance.
University of California San Diego researchers Jacques Verstraete and Sam Mattheus solve longstanding Ramsey problem r(4,t), estimating the solution as t^3. This breakthrough provides a cubic function estimate for finding four people who know each other or t people who don't, shedding light on a century-old math puzzle.
Researchers develop novel feature extraction methods for multigraphs to predict shortest path costs, improving airport operations and sustainability. The proposed statistics-based and learning-based approaches show promising results, outperforming traditional exact search algorithms in computational efficiency.
Aaron Bernstein, a Rutgers professor of computer science, has been selected as a 2024 Sloan Research Fellow for his groundbreaking research on graph algorithms. He plans to use the fellowship to fund a postdoctoral associate on a new project and advance his work on optimizing computational procedures.
Assistant Professor Santiago Segarra at Rice University has won the NSF CAREER Award to develop a new approach for AI-powered climate prediction by leveraging structural properties in real-world data. The research aims to create more effective learning algorithms for structured domains.
Researchers use AI to develop dynamic modeling of brain graphs, capturing dynamics in continuous time for more accurate predictions and personalized treatment of brain diseases. The project aims to track disease development in individual patients and identify biomarkers associated with brain disorders.
Researchers have successfully solved a problem in graph theory that has attracted attention from within the field. The team's research involves packing coloring, which deals with labelling parts of a graph to comply with certain rules and avoid specific conflicts.
Researchers develop AI-based method to quantify cracking patterns in reinforced concrete structures, enabling more accurate and efficient assessments of structural damage. The approach uses graph theory and machine learning algorithms to create a unique 'fingerprint' for each set of cracks, allowing for quick and consistent evaluations.
Researchers at University of Gothenburg developed AI method using graph theory and neural networks to analyze cell movement, enabling better understanding of biological processes and development of new medical technologies. The method can reconstruct cell paths and test medication effectiveness as potential cancer treatments.
Graph computing studies the human world's graphs to analyze and compute them, uncovering hidden information in large-scale data. Key applications include real-time epidemiology analysis and targeted advertising.
A team of CSU researchers has designed a model that can predict which buildings will survive a wildfire, allowing for more effective fire mitigation strategies. By analyzing community networks and incorporating graph theory, the model achieves accuracy rates of up to 86% in predicting building survival.
Researchers at Ohio State University used graph theory to model homeostasis in the human body, predicting changes in dopamine levels and identifying properties of graphs that can help prevent system breakdowns. This approach could lead to targeted medical care for people who need it.
Researchers developed a framework using graph theory to optimize digital communication networks, finding cliques within them for efficient data transmission and improvement in throughput by up to 30%. This approach has far-reaching implications for future networks, including the internet of things with larger volumes of data.
Researchers at MIT have developed a mathematical approach to understanding zeolites, revealing why only a small subset has been discovered or made. The graph-based model predicts which pairs of zeolite types can be transformed from one to the other, opening doors for new pathways in production and potential discoveries of novel materials.
Researchers propose a new graph theory-based paradigm to improve material identification, focusing on topological relationships rather than bond length and angle. This method achieves automatic deduplication for the first time, identifying 626,772 unique structures from 865,458 original structures.
Researchers developed a new computational tool based on Graph theory to infer large-scale regulatory networks from healthy and pathological organs. They were able to pinpoint genes relevant to organ function and potential drivers of diseases, such as type 2 diabetes and Alzheimer's disease.
Engineers used network science to map atomic forces onto a complex graph, simulating macroscopic material behavior. The method simplifies the graph, allowing researchers to replicate the process with other materials.
Researchers at EMBL expand Alan Turing's theory to understand how biological patterns are created, introducing a topological approach that simplifies analysis and predicts properties of Turing systems. This new framework enables the design of networks that can produce desired patterns, with potential applications in tissue engineering.
A research team developed a method for constructing an aggregated model of a power network that can efficiently analyze and control generator groups. The symmetry of the network in graph theory is the fundamental principle for realizing the synchronization of generator groups. This achievement aims to develop analysis and control metho...
Mathematicians at Georgia Institute of Technology have finally solved the 40-year-old Kelmans-Seymour Conjecture in Graph Theory, a field used to model complex networks and optimize connections. The proof required collaboration from six mathematicians over four decades.
A new tool may help predict patients' motor function recovery after stroke by analyzing changes in brain network configuration. Graph theoretical analysis revealed that a lower characteristic path length indicates better recovery, suggesting improved rehabilitation planning and therapy development.
Vojtech Rödl and Mathias Schacht have been awarded the 2012 George Pólya Prize for their work on the regularity method for hypergraphs, producing key results such as the generalized hypergraph removal lemma. The prize recognizes their notable contributions to combinatorial theory.
Lieberman-Aiden's innovations include the 'Hi-C' method for three-dimensional genome sequencing, enabling new understanding of cell state, genetic regulation and disease. He also developed iShoe technology to diagnose balance disorders in the elderly, showcasing his groundbreaking work across genomics, linguistics and more.
Van H. Vu recognized for developing fundamental concentration inequalities applicable to various contexts, including projective geometry and theoretical computer science. He will receive the George Pólya Prize, an engraved medal and a $20,000 cash award.
Researchers use graph theory to analyze Sudoku puzzles, finding that at least 8 of the 9 numbers must appear as given entries for a puzzle to have only one solution. They also explore unsolved problems in graph theory and argue that the number of distinct Sudoku puzzles is around 5.5 billion.
UCSB is part of a $12.5M DARPA-funded consortium researching robust uncertainty management in large networks with complex dynamics. The project aims to develop techniques for predicting the consequences of events like power grid blackouts and improving decision-making under uncertainty.