GitBOM - Repurposing Git's Graph for Supply Chain Security and Transparency
Explore innovative use of Git's graph structure for enhancing software supply chain security and transparency, offering insights into modern cybersecurity practices.
Graph Driven DevSecOps in Practice
Explore graph-driven DevSecOps implementation strategies, enhancing security integration in cloud-native development pipelines for improved efficiency and risk mitigation.
A Blackbox Reduction for Adaptive Adversaries Using Differential Privacy
Explore a novel black-box transformation using differential privacy to convert dynamic algorithms from oblivious to adaptive adversary resistance, enhancing algorithm design for dynamic graphs.
Graph-Oriented Approach for SSTI Payload Optimization - Jinja2 Case Study
Explore a graph-oriented approach for optimizing Server-Side Template Injection payloads, focusing on Jinja2 vulnerabilities and enhancing web application security.
Epidemics on Large Networks - Phase Transitions and Survival Time
Explore epidemic models on large networks, focusing on phase transitions in survival time for deterministic and random graphs. Recent progress and open questions discussed.
Two-Stage Stochastic Matching and Pricing with Applications to Ride Hailing
Explore two-stage stochastic matching and pricing in ride-hailing applications through a mathematical lens. Gain insights from Amin Saberi's conference talk on this cutting-edge topic.
Network Models for Game Theory and Economics - 2004
Explore graph-theoretic models for game theory and economics, covering Nash equilibria, social networks, and connections to probabilistic modeling and inference.
Bayesian Equilibrium: From Local to Global
Explore Bayesian equilibrium concepts from local to global perspectives, covering game theory, measurability, and graph theory applications.
Games as Graphs - A New Approach to Game Theory
Explore game theory through a graph-based approach, gaining new insights into evolutionary game theory and long-run outcomes of replicator dynamics.
Network Centrality Maximization Game
Explore network formation strategies as players optimize PageRank centrality by strategically wiring outgoing links, analyzing equilibria and potential game dynamics.
Understanding Graph Neural Networks - Part 1
Comprehensive introduction to Graph Neural Networks, covering fundamentals, mathematics, and practical implementation using NetworkX and PyG. Ideal for beginners seeking hands-on experience with GNNs.
Building a Knowledge Graph with Spark and NLP for Novel Drug Recommendations
Explore drug discovery using knowledge graphs, NLP, and Spark. Learn techniques for efficient data processing, entity recognition, and relationship extraction to enhance pharmaceutical research.
Henry Adams - Vietoris-Rips Complexes of Hypercube Graphs
Explore Vietoris-Rips complexes of hypercube graphs, their homotopy types, and applications in genetic recombination and persistent homology. Discover formulas, open questions, and conjectures in this fascinating area of applied algebraic topology.
Discrete Homotopy Theory and Applications
Explore discrete homotopy theory's evolution, applications in topology, geometry, and group theory. Learn simplified approaches to fundamental groups and covering spaces, with insights into various mathematical domains.
Learning Distances for Attributed Graphs with Optimal Transport
Explore optimal transport techniques for learning distances between attributed graphs, enhancing graph analysis and comparison methods in data science.
Tractable Network Interventions for Large Socio-Technical Systems
Explore strategies for effective interventions in complex socio-technical networks, focusing on scalable approaches for large-scale systems and their practical applications.
Graph Adjacency Spectral Embeddings - Algorithmic Advances and Applications
Explore algorithmic advances and applications of graph adjacency spectral embeddings, focusing on data science and signal processing innovations in network analysis and representation learning.
Learning from Unreliable Labels via Crowdsourcing
Explore techniques for learning from unreliable labels in crowdsourcing, presented by experts in signal processing and data science for the DEGAS Webinar Series.
Principled Simplicial Neural Networks for Trajectory Prediction
Learn advanced techniques for trajectory prediction using simplicial neural networks, exploring their principles and applications in data science and signal processing.
Distributed Stochastic Non-Convex Optimization - Optimal Regimes and Tradeoffs
Explore optimal regimes and tradeoffs in distributed stochastic non-convex optimization with Usman A. Khan. Gain insights into advanced data science concepts and their applications in signal processing.
Federated Learning in Big Data Over Networks
Explore federated learning techniques for big data networks, focusing on distributed algorithms and privacy-preserving methods in large-scale data analysis.