Graph - Revision history

BPeat at 20:58, 28 April 2024

2024-04-28T20:58:10Z

BPeat at 20:40, 28 April 2024

2024-04-28T20:40:28Z

BPeat at 14:05, 28 April 2024

2024-04-28T14:05:21Z

BPeat at 13:28, 23 March 2024

2024-03-23T13:28:58Z

BPeat at 02:40, 3 March 2024

2024-03-03T02:40:45Z

BPeat at 18:51, 18 September 2023

2023-09-18T18:51:48Z

BPeat at 15:40, 18 September 2023

2023-09-18T15:40:17Z

BPeat at 15:39, 13 September 2023

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BPeat at 00:10, 9 September 2023

2023-09-09T00:10:06Z

BPeat at 19:33, 22 August 2023

2023-08-22T19:33:41Z

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 <youtube>8XJes6XFjxM</youtube>
 <b>The Unreasonable Effectiveness of Spectral Graph Theory: A Confluence of Algorithms, Geometry, and Physics
-</b><br>James R. Lee, University of Washington  [https://simons.berkeley.edu/events/openlectures2014-fall-4 Simons Institute Open Lectures]  Spectral geometry has long been a powerful tool in many areas of mathematics and physics. In a similar way, spectral graph theory has played an important role in algorithms. But the last decade has seen a revolution of sorts:  Fueled by fundamental computational questions, spectral methods have been challenged to address new kinds of problems, and have proved their worth by providing a remarkable set of new ideas and solutions. Starting with a basic physical process (heat diffusion), we will recall how the evolution of the system can be understood in terms of eigenmodes and their associated eigenvalues. The physical view has immediate computational import; for instance, Google models its users as agents diffusing over the web graph. This family of ideas describes the "reasonable" effectiveness of spectral graph theory. But then we will see that spectral objects can also precisely describe other phenomena in a surprisingly unreasonable way. This will lead us to confront some of the most fundamental problems in algorithms and complexity theory from a spectral perspective.
+</b><br>James R. Lee, University of Washington  [https://simons.berkeley.edu/events/openlectures2014-fall-4 Simons Institute Open Lectures]  Spectral geometry has long been a powerful tool in many areas of mathematics and physics. In a similar way, spectral graph theory has played an important role in algorithms. But the last decade has seen a revolution of sorts:  Fueled by fundamental computational questions, spectral methods have been challenged to address new kinds of problems, and have proved their worth by providing a remarkable set of new ideas and solutions. Starting with a basic physical process (heat diffusion), we will recall how the evolution of the system can be understood in terms of eigenmodes and their associated eigenvalues. The physical view has immediate computational import; for instance, Google models its users as agents diffusing over the web graph. This family of ideas describes the "reasonable" effectiveness of spectral graph theory. But then we will see that spectral objects can also precisely describe other phenomena in a surprisingly unreasonable way. This will lead us to confront some of the most fundamental problems in algorithms and complexity theory from a spectral [[perspective]].
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 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
 * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
-* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
+* [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
 * [[Data Governance#Implementing Data Governance with Knowledge Graphs|Implementing Data Governance with Knowledge Graphs]]
 * [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]

@@ Line 123: / Line 123: @@
 = <span id="Knowledge Graph"></span>Knowledge Graph =
-* [[Wikis]]
+* [[Wiki]]
 * [https://www.manning.com/books/ai-powered-search AI Powered Search | Trey Grainger]
 * [https://lucene.apache.org/solr/ Apache Solr] ...open source enterprise search platform built on [https://lucene.apache.org/[https://www.w3schools.com/js/js_json_http.asp Apache Lucene™]. Solr is highly scalable, providing fully fault tolerant distributed indexing, search and analytics. It exposes Lucene's features through easy to use [https://www.w3schools.com/js/js_json_http.asp JSON/HTTP] interfaces or native clients for [https://www.java.com/en/ Java] and other languages. The [https://lucene.apache.org/pylucene/ PyLucene] sub project provides [[Python]] bindings for Lucene.

@@ Line 19: / Line 19: @@
 * [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
-* [[Assistants]] ... [[Personal Companions]] ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]] ... [[LlamaIndex]]
+* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 * [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
 * [[Data Governance#Implementing Data Governance with Knowledge Graphs|Implementing Data Governance with Knowledge Graphs]]

@@ Line 374: / Line 374: @@
 * <b>Expressiveness</b>: Graph databases are expressive because they can model data in a natural and intuitive way that reflects the real world. Graph databases can capture the richness and diversity of data, as well as the semantics and context of relationships. Graph databases can also support various types of queries, such as pattern matching, path finding, subgraph extraction, traversal strategies, etc.
 * <b>Scalability</b>: Graph databases are scalable because they can handle large volumes of data and relationships without compromising performance or quality. Graph databases can distribute data across multiple nodes or clusters, and use parallel processing and caching techniques to optimize query execution². Graph databases can also scale horizontally or vertically, depending on the needs and resources.
-* <b>Performance</b>: Graph databases are performant because they can provide fast and efficient query responses for complex and interconnected data. Graph databases use index-free adjacency, which means that each node stores a pointer to its adjacent nodes, eliminating the need for global indexes or joins. Graph databases can also leverage in-memory computing and graph algorithms to speed up query processing.
+* <b>Performance</b>: Graph databases are performant because they can provide fast and efficient query responses for complex and interconnected data. Graph databases use index-free adjacency, which means that each node stores a pointer to its adjacent nodes, eliminating the need for global indexes or joins. Graph databases can also leverage in-[[memory]] computing and graph algorithms to speed up query processing.
 * <b>Schema evolution</b>: Graph databases support schema evolution because they can adapt to changing data and requirements without affecting existing data or queries. Graph databases allow adding, removing, or modifying nodes, edges, or properties without altering the whole database schema. Graph databases can also provide schema validation and migration tools to ensure data consistency and quality.

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 * [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
 * [[Data Governance#Implementing Data Governance with Knowledge Graphs|Implementing Data Governance with Knowledge Graphs]]
-* [[Architectures]] for AI ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
+* [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
 * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[Telecommunications]] ... [[Computer Networks]] ... [[Telecommunications#5G|5G]] ... [[Satellite#Satellite Communications|Satellite Communications]] ... [[Quantum Communications]] ... [[Agents#Communication | Agents]] ... [[AI Generated Broadcast Content|AI Broadcast; Radio, Stream, TV]]

@@ Line 27: / Line 27: @@
 * [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 * [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Papers Search#Connected Papers | Connected Papers]] using [[Papers Search#Semantic Scholar| Semantic Scholar]] to explore connected papers in a visual graph
 * [https://en.wikipedia.org/wiki/Knowledge_graph Knowledge graph | Wikipedia]

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 * [[Architectures]] for AI ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
 * [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
-* [[Telecommunications]] ... [[Computer Networks]] ... [[Telecommunications#5G|5G]] ... [[Satellite#Satellite Communications|Satellite Communications]] ... [[Quantum#Quantum Internet / Communications|Quantum Internet]] ... [[Agents#Communication | Agents]] ... [[AI Generated Broadcast Content|AI Broadcast; Radio, Stream, TV]]
+* [[Telecommunications]] ... [[Computer Networks]] ... [[Telecommunications#5G|5G]] ... [[Satellite#Satellite Communications|Satellite Communications]] ... [[Quantum Communications]] ... [[Agents#Communication | Agents]] ... [[AI Generated Broadcast Content|AI Broadcast; Radio, Stream, TV]]
 * [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 * [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]