Neo4j with Python Tutorial for Beginners
Learn Neo4j graph database with Python: create nodes, relationships, and queries using Cypher. Explore data analysis, REST APIs, and integration with tools like Docker and FastAPI.
Graph Analysis of Russian Twitter Trolls Using Neo4j
Explore graph analysis of Russian Twitter trolls using Neo4j. Learn about data collection, NLP, graph algorithms, and visualization techniques to uncover social media influence patterns.
Computer Algorithms - 2
The course discusses efficient algorithms from a large number of domains. This course assumes the knowledge of data structures, the knowledge big-O notation and the concept of time and space complexity of an algorithm.
Fundamental Algorithms - Design and Analysis
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems.
SAP HANA Graph Capabilities and Community Challenge - Deep Dive
Deep dive into SAP HANA Graph capabilities, exploring use cases, benefits, and a hands-on community challenge. Learn about graph models, pattern matching, algorithms, and architecture.
Neo4J GDS: Node Embeddings Algorithms en Español
Explora Neo4J y la librería GDS para generar embeddings en grafos, aplicando algoritmos de ciencia de datos y aprendizaje automático.
Neo4J GDS: Algoritmos de Detección de Comunidades en Español
Explora algoritmos de detección de comunidades en Neo4J GDS para ciencia de datos y aprendizaje automático, con ejemplos prácticos en español.
Small Space Differentially Private Graph Algorithms in the Continual Release Model
Explore sublinear space differentially private graph algorithms for continual release, covering densest subgraphs, k-core decomposition, maximum matching, and vertex cover.
GPU-Acceleration for Pandas, NetworkX, and Apache Spark MLlib - Accelerating Data Science Tools
Discover GPU-accelerated data science tools for pandas, NetworkX, and Apache Spark MLlib. Learn about RAPIDS cuDF, accelerated data processing, and optimizing machine learning pipelines for enhanced performance and accessibility.
Graph Algorithms and Graph Theory - Session 1B
Explore advanced concepts in graph algorithms and theory, delving into cutting-edge research and applications presented at FOCS 2020.
Graph Algorithms and Graph Theory - Session 7A
Explore advanced concepts in graph algorithms and theory, delving into cutting-edge research and applications presented at the FOCS 2020 conference.
Introduction to Length-Constrained Expanders and Expander Decompositions
Explore length-constrained expanders and expander decompositions, powerful tools for fast graph and network optimization algorithms. Learn their applications and potential in solving complex problems.
Szemerédi Regularity Lemma in Graph Machine Learning
Exploring the Szemerédi regularity lemma's application in graph machine learning, focusing on its theoretical foundations and practical implications for AI-driven graph analysis.
New Frontiers in Structure vs Randomness with Applications to Combinatorics, Complexity, and Algorithms
Explore new frontiers in structure vs randomness, delving into applications in combinatorics, complexity, and algorithms. Gain insights on Erdos-Turan problem and recent advancements in additive combinatorics.
Towards Foundation Models for Graphs
Explore cutting-edge research on foundation models for graph data structures, examining their potential applications and implications for AI and machine learning.
Efficient Community Search Based on Relaxed k-truss Index
Explore advanced techniques for efficient community search in networks using relaxed k-truss indexing methods and graph-based algorithms for optimized data analysis.
Multi-Stage Vertex-Centric Programming for Agent-Based Simulations
Explore multi-stage vertex-centric programming for agent-based simulations, improving performance and control flow in complex graph algorithms through meta-programming techniques.
Fine-Grained Complexity - Lecture 1
Explore cutting-edge research in fine-grained complexity theory with MIT expert Virginia Vassilevska Williams, delving into advanced algorithmic concepts and their applications.
Graph Data Structures - Part 2
Explore advanced graph data structures and optimization techniques for designing efficient algorithms in this in-depth lecture by Thatchaphol Saranurak.
Graph Data Structures Part 3
Explore advanced graph data structures and optimization techniques for designing efficient algorithms, presented by Thatchaphol Saranurak from the University of Michigan.
Graph Data Structures - Part 1
Explore fundamental graph data structures and their applications in algorithmic optimization, presented by a leading expert from the University of Michigan.