#NetworkX

[damilola-doodles]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[dammyanimation]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[damilola-ai-automation]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[damilola-warrior-mindset]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[damilola-moyo]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[rauhauser]

GraphCraft Summer

Each moment arises from the causes and conditions of the prior moment, that's what Buddhists call Pratītyasamutpāda. When it occurs in our lives to the point that we stop and rub our eyes, the most common phrase I'd use to describe that is "inflection point". This is on my mind because a number of them have come together all at once, here at the end of May, 2025.

Earlier this week, for the first time in a very long time, I opened up the Maltego graph associated with Shall We Play A Game?

This has been stalled by my partial conversion of the original Evernote information to Obsidian, and just life stuff. Now there are four new green dots there, and this is notable because they are someone else's work, not mine.

That is not someone I have contact with (yet), and that may never happen. I just noticed what they were doing, saw that it overlapped with my thinking, and I was instantly fascinated.

The burgundy dots at the top represent mostly finished pieces, eleven of the forty nine that will eventually be found. Things have changed since I started on them, they'll be getting some updates.

Two things have changed that are far outside my control. One of them happened not far from this marker on the Embarcadero. This is a pedestrian friendly space, the other change is much more pedestrian - I noticed that ArangoDB now has a persistent NetworkX connector. That's one less obstacle I have to scale, as things scale up.

[arbitcoin]

MIRA Network Ecosystem Map Disclosure: Behind the Conses of AI

أصدرت شبكة ميرا مؤخرا أحدثها "خريطة النظام البيئي"، تجمع هذه الخريطة أكثر من 25 مشروعًا تعاونًا حقيقيًا في 6 حقول رئيسية ، حيث تقدم بالكامل التنفيذ الفعلي لميرا كطبقة التحقق اللامركزية. بالنسبة إلى ميرا ، هذا ليس مجرد قائمة بالتعاون ، بل إن تصويرًا للموثوقية ، فإنه يمكن أن يمارس أي شيء من هذا الفيلم ، ما إذا كان ذلك هو أن يرى هذا الفيلم ، حيث كان هناك ما يمكن أن يفسده من طبقاتها. هو وكلاء الذكاء الاصطناعي ، أو تكنولوجيا التعليم ، أو أدوات تداول التشفير ، لعبت ميرا بهدوء دور في مختل�� المجالات لمساعدة مطوري التطبيقات في ضمان صحة نتائج الإخراج. التحقق من كل شيء ✦ pic.twitter.com/ptdhru0wby - ميرا (mira_network) 28 مايو 2025 ست مناطق رئيسية ، تخطيط كامل طبقة التطبيق (التطبيقات)قامت أكثر من 10 تطبيقات بما في ذلك Gigabrain و Learnrite و Creato و Astro بدمج واجهة برمجة تطبيقات MIRA في عملية المنتج لجعل المحتوى الذي تم إنشاؤه بواسطة الذكاء الاصطناعي أكثر جدارة بالثقة. مشاريع مفتوحة المصدر (مشاريع OSS)كما تختار المشاريع المصدر مفتوحة المصدر مثل Wikisentry و Kaito-Tok و Veraquiz التواصل مع مكدس Technology Mira لجعل محتوى المعرفة من تعاون المجتمع أكثر مصداقية. أطر عمل الوكيلتسمح المنصات مثل Sendai و Zerepy و ARC و AICRAFT للمطورين بتسليم طبقة التحقق من MIRA للتفتيش قبل أداء مهام الوكيل على السلسلة. شركاء النظام الإيكولوجييجمع Plume و Monad و Kernel و Lagrange وشركاء طبقة البروتوكول الأخرى مع طبقة Mira Trust لتعزيز تعميم تكنولوجيا التحقق اللامركزية. النماذجأصبح Openai ، Anthropic ، Meta ، Nous ، Sentient ، Deepseek وغيرهم من الموردين النماذج الكبار الأساس الحاسوي لتشغيل طبقة التحقق من ميرا. البيانات والحسابتتيح مصادر مثل EXA و Reddit و Delphi و Bespoke Rag ، وما إلى ذلك ، وشركاء الحوسبة في GPU مثل الزائدين ، Aethir ، و Ionet MIRA الحفاظ على التشغيل عالي السرعة في بيئات التطبيقات المختلفة. ميرا: اندفاع غير مرئي ل AI تم التحقق منه كل شعار على الخريطة هو مشروع خاص تم دمج ميرا حقًا أو يتم تنفيذه. من وكلاء الذكاء الاصطناعى إلى التكنولوجيا التعليمية وحتى أدوات المعاملات ، توفر طبقة التحقق اللامركزية في ميرا بصمت الحماية وراء الكواليس ، مما يقلل من الإخراج الوهم وضمان نتائج موثوقة. يعتقد فريق MIRA أن أدوار طبقة التحقق هذه ، مثل آلية الإجماع في blockchain ، أصبحت المعدات الأساسية لتطبيقات الذكاء الاصطناعي. إنها ليست مجرد "بوضوح" مثل الهيكل على السلسلة ، لكنه لا يقل أهمية. لا يقتصر على التشفير ، ولكن يمتد إلى الحياة اليومية ولدت ميرا في عالم التشفير ، لكن مشاريع التعاون لم تعد تقتصر على Web3. لا تؤكد العديد من التطبيقات على "blockchain" على الإطلاق ، ولكن لا تزال تستخدم طبقة التحقق من ميرا. هذا يعني أيضًا أن البنية التحتية اللامركزية بدأت في التحرك نحو التطبيقات السائدة وأصبحت مؤس��ة ثقة لا غنى عنها للمنتجات التي تحركها الذكاء الاصطناعي. قالت ميرا إنه سيكون هناك المزيد من مشاريع التطبيق في المستقبل ، واختيار بناء الذكاء الاصطناعي الموثوق به على طبقة التحقق الخاصة به. خريطة النظام الإيكولوجي هذه هي مجرد البداية. Discord ：https://discord.com/invite/mira-networkx ：https://x.com/miranetworkcn (تحليل المقال الخاص في Messari: كيف يستخدم بروتوكول ميرا آلية الإجماع المركزي السابق لجعل الذكاء الاصطناعي أكثر صدقًا؟) تحذير المخاطراستثمارات العملة المشفرة محفوفة بالمخاطر للغاية ، وقد تتقلب أسعارها بشكل كبير وقد تفقد كل مديرك. يرجى تقييم المخاطر بحذر.

[news786hz]

A Step-by-Step Guide to Build an Automated Knowledge Graph Pipeline Using LangGraph and NetworkX

A Step-by-Step Guide to Build an Automated Knowledge Graph Pipeline Using LangGraph and NetworkX

[souhaillaghchimdev]

Social Network Analysis Programming

Social Network Analysis (SNA) is a powerful technique used to explore and analyze the relationships between individuals, groups, or entities. With programming, we can visualize and calculate complex network structures, which is useful in fields like sociology, marketing, cybersecurity, and even epidemiology.

What is Social Network Analysis?

Social Network Analysis is the process of mapping and measuring relationships and flows between people, groups, organizations, computers, or other information/knowledge processing entities. It reveals the structure of networks and helps identify influential nodes, communities, and patterns.

Key Concepts in SNA

Nodes (Vertices): Represent entities (e.g., people, computers).

Edges (Links): Represent connections or relationships between nodes.

Degree Centrality: Number of direct connections a node has.

Betweenness Centrality: How often a node appears on shortest paths.

Clustering: Grouping of nodes based on similarity or proximity.

Tools & Libraries for SNA Programming

Python: Powerful language with strong libraries like NetworkX and Pandas.

NetworkX: Used to create, manipulate, and visualize complex networks.

Gephi: GUI-based open-source software for large network visualization.

Graph-tool (Python): Fast and efficient network analysis for large graphs.

D3.js: JavaScript library for dynamic and interactive network visualizations.

Example: Basic Network Analysis with Python & NetworkX

import networkx as nx import matplotlib.pyplot as plt # Create a graph G = nx.Graph() # Add nodes and edges G.add_edges_from([ ('Alice', 'Bob'), ('Alice', 'Carol'), ('Bob', 'David'), ('Carol', 'David'), ('Eve', 'Alice') ]) # Draw the network nx.draw(G, with_labels=True, node_color='lightblue', edge_color='gray') plt.show() # Analyze print("Degree Centrality:", nx.degree_centrality(G)) print("Betweenness Centrality:", nx.betweenness_centrality(G))

Applications of Social Network Analysis

Marketing: Identify key influencers and optimize content spread.

Security: Detect suspicious communication patterns or malware spread.

Epidemiology: Track the spread of diseases across populations.

Sociology: Understand community structures and social behavior.

Recommendation Systems: Suggest friends, content, or connections.

Tips for Effective SNA Programming

Start with clean and structured data (CSV, JSON, etc.).

Visualize early and often to detect patterns.

Use metrics like centrality and clustering coefficients for deeper insights.

Leverage real-world datasets like Twitter or Facebook data via APIs.

Scale with performance-optimized libraries for large datasets.

Conclusion

Social Network Analysis Programming unlocks a new dimension in data analysis by focusing on relationships rather than isolated entities. With the right tools and mindset, you can uncover hidden structures, detect influence patterns, and make data-driven decisions in a connected world.

[renatoferreiradasilva]

Problema Geral: Como a aplicação de técnicas de clusterização estatística pode identificar padrões ocultos de violência armada nos bairros de alta criminalidade de Chicago (como Englewood e South Shore), integrando dados balísticos, socioeconômicos e geográficos para subsidiar políticas de prevenção e desmantelar redes criminosas, diante da fragmentação de dados e da dinâmica territorial das gangues?

Contextualização do Problema:

Chicago registrou 650 homicídios em 2023, concentrados em bairros periféricos marcados por disputas de gangues, tráfico de armas e segregação racial. Apesar dos investimentos em sistemas como o ShotSpotter (detecção de tiros em tempo real) e o CLEAR (banco de dados criminal do Departamento de Polícia), persistem desafios:

Fragmentação de dados: Informações sobre projéteis recuperados, marcas de armas e ligações entre crimes ficam dispersas em sistemas não integrados.

Dinâmica territorial das gangues: A rápida mudança de "territórios" controlados por grupos como os Gangster Disciples ou Black Disciples dificulta a análise temporal de padrões.

Viés racial na coleta: Bairros negros e latinos são hipervigilados, gerando dados distorcidos que podem reforçar estereótipos em modelos algorítmicos.

A falta de uma análise integrada impede identificar, por exemplo, se um mesmo calibre de arma (.40 S&W) está sendo usado em múltiplos homicídios por diferentes gangues, ou se há correlação entre desertificação escolar e picos de violência em áreas específicas.

Objetivo Central:

Desenvolver um modelo de clusterização geo-temporal para:

Mapear "hotspots evolutivos" de violência armada, correlacionando dados balísticos (ex.: tipo de arma, trajetória de projéteis) com variáveis socioeconômicas (ex.: desemprego juvenil, densidade populacional).

Identificar redes de circulação de armas através de agrupamentos de munições e projéteis com marcas de ferrolho similares.

Antecipar conflitos entre gangues detectando padrões espaço-temporais em disparos e homicídios.

Questões-Chave para Investigação:

Como integrar dados do ShotSpotter (localização de tiros), CLEAR (registros criminais) e ISTA (análise balística) em um único pipeline de clusterização?

Quais algoritmos (ex.: ST-DBSCAN para dados espaço-temporais, Spectral Clustering para redes complexas) são mais eficazes para lidar com a mobilidade territorial das gangues?

Como evitar que clusters reforcem estigmas sobre bairros específicos (ex.: rotulando Englewood como "zona de risco" sem considerar investimentos comunitários em mediação de conflitos)?

De que forma a clusterização pode apoiar políticas de violence interruption (ex.: identificar cruzamentos com maior recorrência de tiroteios para posicionar equipes de mediação)?

Relevância do Problema:

Para a segurança pública: Detectar se 70% dos homicídios em Austin (bairro de Chicago) usam armas de mesmo calibre pode indicar uma fonte comum de tráfico.

Para a justiça social: Correlacionar clusters de violência com indicadores como falta de iluminação pública ou fechamento de escolas evidencia determinantes estruturais.

Para a tecnologia cívica: Modelos open-source de clusterização podem ser usados por coletivos como GoodKids MadCity para pressionar por políticas baseadas em evidências.

Hipóteses de Solução:

Clusterização híbrida: Combinar dados balísticos (ex.: marcas de ferrolho em projéteis) com grafos de relações interpessoais de gangues (via Gephi ou NetworkX).

Parcerias comunitárias: Treinar líderes locais na interpretação de clusters via plataformas como Tableau Public, gerando mapas interativos acessíveis.

Integração com IA preditiva: Usar clusters históricos para treinar modelos que alertem sobre riscos de confrontos em eventos comunitários (ex.: festas de rua).

Exemplo Prático de Clusterização:

Dados de Entrada:

1.200 tiroteios registrados no South Side (2023).

Variáveis: calibre da arma, horário, coordenadas GPS, proximidade de escolas/bares, perfil demográfico da vítima.

Processo:

Pré-processamento: Normalizar dados de calibre (numérico) e tipo de local (categórico) usando one-hot encoding.

Clusterização com ST-DBSCAN:

Cluster 1: Tiroteios às 2h-4h, calibre 9mm, próximo a bares → Padrão de violência associado a conflitos entre gangues por controle de pontos de venda.

Cluster 2: Disparos às 15h-17h, calibre .45, próximo a escolas → Possível recrutamento de adolescentes ou disputas territoriais diurnas.

Ação: Alocar mediadores de conflitos da Institute for Nonviolence Chicago nos horários e locais identificados.

Desafios Éticos e Técnicos:

Privacidade: Evitar a exposição de dados individuais de vítimas ou suspeitos.

Viés algorítmico: Dados históricos da polícia podem superestimar violência em bairros já vigiados.

Latência: Atualização em tempo real de clusters para responder a mudanças nas dinâmicas das gangues.

Palavras-Chave:

Clusterização geo-temporal, violência armada em Chicago, análise balística preditiva, ST-DBSCAN, mediação comunitária, justiça algorítmica.

Implicação Final: A clusterização não é neutra: pode tanto perpetuar ciclos de vigilância repressiva quanto empoderar comunidades com dados para reivindicar políticas de segurança baseadas em reparação, não em repressão. Em Chicago, onde a geografia da violência reflete décadas de racismo estrutural, essa dualidade é inescapável.

[damilola-doodles]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…

[dammyanimation]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…

[damilola-ai-automation]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…

[damilola-warrior-mindset]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…

[damilola-moyo]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…