Discriminative Prototype Selection for Graph Embedding
Explore graph embedding techniques, focusing on discriminative prototype selection methods for improved graph representation and matching in various applications.
Bite-Sized Neo4j for Data Scientists
Learn Neo4j for data science: connect to databases, use Python drivers, write Cypher queries, import data, perform graph analytics, and compare with SQL databases.
Genus de Grafos de Pancakes y Pancakes Quemados: Cotas Superiores e Inferiores
Explora cotas superiores e inferiores para el genus de grafos de pancakes y sus aplicaciones en bioinformática y computación paralela, utilizando sistemas de rotaciones y técnicas de permutación.
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.
Graph Embeddings - Ways Your AI Can Learn From Your Connected Data - Nicolas Rouyer
Explore five ways AI can leverage graph embeddings to extract insights from connected data, enhancing machine learning algorithms and unlocking hidden patterns in complex relationships.
Topological Node2vec: Improving Graph Embeddings with Persistent Homology
Exploring persistent homology to enhance graph embeddings, combining topology with machine learning for improved data analysis and representation in complex networks.
Graph Convolutional Networks - GNN Paper Explained
Comprehensive exploration of Graph Convolutional Networks, covering theory, implementation, and applications. Delves into spectral methods, Weisfeiler-Lehman perspective, and GNN depth, offering insights for both beginners and experts.
Task-Driven Network Discovery via Deep Reinforcement Learning on Embedded Spaces
Explore task-driven network discovery using deep reinforcement learning on embedded spaces. Learn about improving network observability and optimizing data collection for accurate analysis.
Angel 3.0 - A Full Stack Machine Learning Platform
Explore Angel 3.0, a comprehensive machine learning platform featuring auto feature engineering, hyperparameter tuning, model serving, and PyTorch integration for enhanced graph embedding and GNN algorithms.
Inapproximability of Clustering in Lp Metrics
Explores computational complexity of clustering in Lp metrics, discussing approximation algorithms, hardness of approximation, and key concepts like Johnson Coverage Hypothesis.
GraphRAG: The Most Incredible RAG Strategy Revealed
Explore GraphRAG, an advanced retrieval-augmented generation system enhancing AI responses with relevant context. Learn implementation, setup, and advantages over traditional methods.
Leveraging Graphs for AI
Explore graph theory's role in AI, from data structures to machine learning, with practical examples and applications in analytics, algorithms, and neural networks.
The Power of Knowledge Graphs and AI - A New Era for Developers and Data Scientists
Explore Knowledge Graphs and AI: Learn technical aspects, real-world applications, and future trends. Discover how to leverage these technologies for data harmonization and inference.
Saga: Continuous Construction and Serving of Large Scale Knowledge Graphs
Explore continuous construction and serving of large-scale knowledge graphs with Saga, an end-to-end platform. Learn about challenges in industrial settings and various graph services.
Geometric Deep Graph Learning: Exploring Opportunities in Different Geometric Spaces
Explore geometric deep graph learning in non-Euclidean spaces. Discover properties of hyperbolic and hyperspherical spaces for enhanced graph representation and analysis across various applications.
DeepWalk: Online Learning of Social Representations
Explore online learning of social representations with DeepWalk, a groundbreaking approach to understanding social networks and graph structures.