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Graph Theory For Visual Learners - by Tivadar Danka

The post introduces graph theory through examples involving city traffic, mathematics, geometry, and DNA. It explains nodes, edges, degree, isomorphic drawings, walks, and paths, then recounts Euler’s Königsberg bridge problem.

27 mins read Listed 3 days ago in Substack: The Palindrome

memgraph.com Tutorial

Graph Algorithms Cheat Sheet For Coding Interviews

An introductory guide to graph data structures and three core algorithms (BFS, DFS, Dijkstra) with definitions, operations, real-world example (Facebook Graph API), and code examples in Python.

20 mins read Listed 9 days ago in X

arxiv.org Scientific research

[2611.10385] A Topological Encoder Decoder Framework for Temporal Graph Learning

The paper presents TOPOGED, a method that predicts a future graph’s topology before reconstructing a plausible snapshot, including changes in nodes and structure. Across 14 datasets, it reports stronger node forecasting, graph-structure results, and edge prediction metrics than baseline methods.

1 min read Listed 4 days ago in Bluesky

www.franksworld.com Blog post

Enhancing AI Agent Predictability with Graph Engineering – Frank's World of Data Science & AI

Graph engineering provides AI agents with explicit graph-based work maps to improve predictability, governance, and portability. Neo4j demonstrates a knowledge layer that integrates graph constructs with existing data systems and showcases practical use in loan approvals; the approach emphasizes skill and memory graphs to enhance decision-making and adaptability.

3 mins read Listed 10 days ago in RSS: Frank's World of Data Science & AI

nebula-graph.io Tutorial

What Is a Graph Database? A Complete Guide for 2026

An introduction to graph databases, detailing how they store nodes and edges with properties, how traversal queries differ from relational joins, and why they enable scalable, connected data reasoning. It highlights GraphRAG, graph query languages, and native solutions like NebulaGraph for large-scale use cases.

11 mins read Listed 12 days ago in X

link.amazon Book

Graph Engineering — The Loop Memory Model: Give agentic workflows durable memory, traceability, and shared knowledge with graphs : Pavlyshyn, Volodymyr: Amazon.de: Books

A guide to building agentic memory using a two-graph model: separate work history from durable knowledge, connect them with bridges, and query a disposable graph index. It covers data parsing, provenance, concurrent updates, evaluation of graph quality, and practical decisions on when simpler storage may suffice.

2 mins read Listed 9 days ago in X

www.amazon.de Book

Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch : Labonne, Maxime: Amazon.de: Books

This book teaches graph neural networks with Python and PyTorch Geometric, starting from graph theory basics to advanced architectures like graph convolution and self-attention, and applies them to real-world tasks such as classification and link prediction. It includes project workflows, datasets from tabular data, and online code, aimed at practitioners and students building GNN-based applications.

2 mins read Listed 9 days ago in X

www.amazon.de Book

Graph Data Modeling in Python: A practical guide to curating, analyzing, and modeling data with graphs : Hutson, Gary, Jackson, Matt: Amazon.de: Books

The book guides designing and implementing graph data models in Python using NetworkX, igraph, and Neo4j, with practical use cases and guidance from beginner to advanced levels. It covers converting relational data to graphs, managing graph databases, and applying graph analytics such as community detection and recommendations for analysts, developers, and data scientists.

2 mins read Listed 9 days ago in X

github.com Tools and repositories

GitHub - Graphicle-AI/graphicle: Code graph and memory for your coding agents, in one Rust program. For Claude Code, Codex, Cursor and any MCP client. · GitHub

Graphicle is a single Rust program that builds a code graph and memory for coding agents, runs locally without telemetry, and integrates with multiple coding assistants. It provides cited code excerpts from exact files and lines, supports setup and undo operations, and offers a terminal workflow to query the graph and memory. Performance claims compare favorably to Graphify across several codebases.

17 mins read Listed 28 days ago in Bluesky

neo4j.com Tools and repositories

NODES 2026 | Engineering Better Intelligence

GraphRAG enhances question answering by mapping connections in a graph and traversing relationships to provide complete context with fewer tokens. It supports memory for agents, integrates with existing frameworks, and uses ontology to enforce business rules across dispersed data.

1 min read Listed 16 days ago in Bluesky