Graph Analytics with ArangoDB
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This document discusses using graphs and graph databases for machine learning. It provides an overview of graph analytics algorithms that can be used to solve problems with graph data, including recommendations, fraud detection, and network analysis. It also discusses using graph embeddings and graph neural networks for tasks like node classification and link prediction. Finally, it discusses how graphs can be used for machine learning infrastructure and metadata tasks like data provenance, audit trails, and privacy.
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Graph Analytics with ArangoDB
- 2. tl;dr Graph Analytics Answer questions from Graph Data 2 Graph Embeddings and Graph Neural Networks Learning Graphs Graph-based Machine Learning Metadata Utilizing Graphs for Operating ML Infrastructure https://dzone.com/articles/graph-databases-machine-learning
- 3. Challenge...
- 4. Agenda ML Infrastructure & Metadata Graphs Graph Database Graph Analytics Graph Embeddings Graphs Neural Networks Part 2
- 6. ● ● ● ● ●
- 7. This workshop... 7 … is for you! Please share ● Expectations ● Questions ● Feedback ● Ask for breaks if needed ● …. … is also virtual! ● Let us work together in these times!
- 10. Why should you care? 10 https://towardsdatascience.com/predictions-and-hopes-for-graph-ml-in-2021-6af2121c3e3d
- 11. What problems can we solve? Graph Analytics Answer questions from Graph - Community Detection - Recommendations - Centrality - Path Finding - Fraud Detection - Permission Management - ... 11 Graph Embeddings and Graph Neural Networks Learning Graphs - Node/Link Classification - Link Prediction - Classification of Graphs - ... Graph-based Machine Learning Metadata Utilizing Graphs for Operating ML Infrastructure - Data Provenance - Audit Trails - Privacy (GDPR/CCPA) - ,,,
- 12. Agenda ML Infrastructure & Metadata Graphs Graph Database Graph Analytics Graph Embeddings I Graphs Neural Networks
- 13. Graph Analytics with ArangoDB Graph Data Model ● Connections are first class citizens ● Vertices and Edges ● Native or build on top of other data models 13
- 14. Graph Analytics with ArangoDB Graph Properties ● (un)directed ○ Facebook vs Twitter ● weighted ● Sparse/Dense ● (a)cyclic Graph Queries ● Traversals ● Search ● Graph Algorithms 14
- 15. Optional Lab: Graphs & Properties https://colab.research.google.com/github/joerg84/Graph_Powered_ML_Workshop/blob/master/Graph_properties.ipynb
- 18. 18 AQL - A Query Language That Feels Like Coding ● Common query language for all data-models ● Aims to be human-readable ● Same language for all clients, no matter what programming language people use ● Easy to understand for anyone with an SQL background FOR c IN company FILTER c.name == @companyName FOR department IN 1..6 INBOUND c isPartOf RETURN { c: c.name, department: department.name, ordered: ( FOR o IN orders FILTER o.contact == department.contact RETURN {date: o.date, amount: o.amount} ) }
- 19. FOR d IN v_imdb SEARCH ANALYZER(d.description IN TOKENS('amazing action world alien sci-fi science documental', 'text_en') || BOOST(d.description IN TOKENS('galaxy', 'text_en'), 5), 'text_en') SORT BM25(d) DESC LIMIT 10 FOR vertex, edge, path IN 1..1 INBOUND d imdb_edges FILTER path.edges[0].$label == "DIRECTED" RETURN DISTINCT { "director" : vertex.name, "movie" : d.title } ArangoSearch is a powerful search and similarity ranking engine natively integrated into ArangoDB. Combine search with any other data model. 19 ArangoSearch
- 20. Property-Graph-Model Languages ● Tinkerpop/Gremlin ● Cypher ● AQL ● ... ● subject, predicate, and object ● No internal structure of nodes/edges ● Languages ● SPARQL 20 Person name: Max City location: born_in year: 1984 --- RDF Triple Store Ontologies & Logic for Inference
- 21. 21 https://w3c.github.io/rdf-star/ <<:bob foaf:age 23>> ex:certainty 0.9 . SELECT ?p ?a ?c WHERE { <<?p foaf:age ?a>> ex:certainty ?c . } Support - Convert to plain RDF (tool) - Optimized storage/processing - Conversion to PG (tool) Max Job1 start end empl oyer
- 23. Graph Modelling Edge Attribute Vertex Attribute 23 Person name: Max rated rating: 5 --- Person name: Max Movie: Free Solo: Movie: Free Solo Rating rating: 5 gave rated_by
- 24. Lab: Property Graph Queries https://colab.research.google.com/github/joerg84/Graph_Powered_ML_Workshop/blob/master/Graphs_Queries.ipynb
- 25. Graph Analytics with ArangoDB 25 http://btimmermans.com/2017/12/11/machine-learning-overview/
- 28. Why Graph?
- 29. Knowledge Graphs and Machine Learning
- 30. Graph Algorithms ● Search/Traversal ○ Find a node/edge ○ BFS/DFS (already covered) ● Pathfinding ○ How to get from a to b ● Centrality ○ What are the important nodes (e.g., influencer) in a network? ● Cycle Detection ○ Deadlock Detection ○ Network Analysis ● Community Detection ○ Are there subgroups? 30
- 31. Shortest Path ● Shortest Path ○ Dijkstra ○ Bellman-Ford ● K shortest path ● Single Source Shortest path ● All-Pairs Shortest Path 31 https://towardsdatascience.com/10-graph-algorithms-visually-explained-e57faa1336f3
- 32. Minimal Spanning Tree ● Network Broadcast/routing ● Image segmentation ● Algorithms ○ Prim’s algorithm ■ Extend from random start vertex ○ Kruskal’s algorithm ■ Keep choosing cheapest edges as long as it doesn’t create a cycle 32 https://towardsdatascience.com/10-graph-algorithms-visually-explained-e57faa1336f3
- 35. Cycle Detection ● Deadlock Detection ● Network Analysis ● Algorithms ○ DFS ○ Floyd’s algorithm ■ tortoise and the hare algorithm ○ Brent’s algorithm ○ Johnson’s algorithm 35 https://towardsdatascience.com/10-graph-algorithms-visually-explained-e57faa1336f3
- 36. Community Detection ● Triangle Count ● (Strongly )Connected Components ○ Kosaraju’s algorithm ○ Tarjan’s algorithm ● Label Propagation ● Application ○ Social Networks ○ Clustering ○ … https://networkx.github.io/documentation/stable/r eference/algorithms/community.html 36
- 37. Topological Sort ● ● ● ● Applications ○ Dependencies ○ Scheduling ■ E.g., Makefiles 37
- 39. Centrality ● Degree Centrality ○ How many in/outgoing connections ● Closeness Centrality ○ Average closeness to all nodes ● Betweenness Centrality ○ Connecting subgroups ○ How often is node on shortest path ● PageRank ○ Transitive Influence 39 https://www.arangodb.com/docs/stable/graphs-pregel.html#vertex-centrality
- 40. 40 https://networkx.github.io/ Graph ToolBox ● Load and store graphs ● Analyze network structure ● Build network models ● Design new network algorithms ● Visualize ● ...
- 43. Graph Analytics with ArangoDB 43 Fraud Detections Panama papers Enterprise Hierarchies Permission Management Internet Of Things Bill of Materials Representation Learning ...
- 47. 47 User Movie Rates I Collaborative Filtering “Find highly rated movies, by people who also like movies I rated highly” 1. Find movies I rated with 5 stars 2. Find users who also rated these movies also with 5 stars 3. Find additional movies also rated 5 stars by those users
- 51. 51 PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites. Google https://en.wikipedia.org/wiki/PageRank
- 52. 52 Goal: How likely a random surfer will end up at a page? - Random walk across link graph - Iteratively distributing rank to neighbouring nodes https://en.wikipedia.org/wiki/PageRank https://stanford.edu/~rezab/classes/cme323/S15/notes/lec8.pdf
- 56. Thanks for listening! Reach out with Feedback/Questions! • @arangodb • https://www.arangodb.com/ • docker pull arangodb https://www.udemy.com/course/getting-started-with-arangodb/