#NetworkAnalysis

[ppcseo]

#criticalthinking #determiningnetworkrequirements #networkdesign #networkanalysis #usergrowth #deviceconnectivity #branchlocations #remotework #bandwidthneeds #qualityofservice #businessapplications #emailusage #filesharing #resourceintensivesoftware #databaseusage #onlinemeetings #VoIPphones #videosurveillance #internetconnections #securityenhancement #networkfirewalls #endpointprotection #intrusiondetection #VPNconcentrators #accesscontrols #activitymonitoring #redundancy #networkuptime #internetoutages #technicalsystems #securityplatforms #scalableinfrastructure

[guillaumelauzier]

Graph Neural Networks: Revolutionizing Data Analysis in Graph-Structured Domains

Graph Neural Networks (GNNs) represent a paradigm shift in the realm of neural networks, uniquely tailored for graph-structured data. They are pivotal in addressing complex data scenarios where traditional neural networks fall short. This comprehensive article delves into the core functionalities, applications, and future potential of GNNs.

Understanding Graph Neural Networks

Direct Application to Graphs GNNs' foremost strength lies in their direct application to graphs, facilitating node-level, edge-level, and graph-level prediction tasks. This flexibility proves invaluable across various fields where data is intrinsically relational, such as analyzing social networks, understanding molecular structures, and optimizing communication networks . Processing Complex Graph-Structured Data GNNs excel at processing and analyzing intricate graph-structured data. This capacity unlocks new avenues in numerous domains, including network analysis, computational biology, and the development of advanced recommender systems . Dependence on Graph Structure Central to GNNs' functionality is their ability to capture the dependence of graphs through message passing between nodes. By leveraging the inherent structural information of graphs, GNNs can make more accurate predictions and analyses, a critical aspect in fields like network security and structural health monitoring .

Expansive Applications of GNNs

Versatility in Various Fields GNNs' adaptability to graph data makes them invaluable in areas where relationships and connections are crucial. This includes, but is not limited to, social network analysis, drug discovery and chemistry, traffic flow prediction, and biological network analysis . From Foundations to Frontiers Spanning from basic concepts to cutting-edge advancements, GNNs are continually evolving. Ongoing research and development are likely to amplify their capabilities, making them even more effective in handling diverse, graph-related challenges .

How can Graph Neural Networks be used in Generative Art?

Graph Neural Networks (GNNs) have significant potential in the realm of generative art, leveraging their unique capabilities in understanding and manipulating graph-structured data. Here are some ways GNNs can be applied in this field: - Modeling Complex Relationships: GNNs can model intricate relationships and patterns within data. In generative art, they can analyze the structure of artistic elements, like color, form, and composition, to generate new artworks that maintain stylistic coherence or offer novel artistic interpretations. - Link Prediction for Artistic Elements: GNNs are adept at inferring missing links or detecting spurious ones in graph data. This capability can be used in generative art to predict and create connections between different artistic elements, leading to the generation of visually cohesive and complex artworks . - Learning Node Embeddings: In the context of generative art, GNNs can learn embeddings (representations) of various artistic elements. These embeddings can capture the nuances of style, technique, and other artistic features, which can then be used to generate new art pieces that reflect certain styles or artistic trends . - Message Passing for Artistic Interpretation: GNNs use message passing to understand graph structures, which can be applied to the way different elements in an artwork relate to each other. This can help in creating art that dynamically changes or evolves based on certain rules or inputs, adding an interactive or adaptive element to the artwork .

Python code example of a Graph Neural Networks

Here's a basic example of implementing a Graph Neural Network (GNN) using PyTorch. This code demonstrates the creation of a simple GNN for node classification on a graph: import torch import torch.nn as nn import torch.nn.functional as F from torch_geometric.nn import GCNConv # Define a simple GNN model class GCN(nn.Module): def __init__(self, num_features, num_classes): super(GCN, self).__init__() self.conv1 = GCNConv(num_features, 16) self.conv2 = GCNConv(16, num_classes) def forward(self, data): x, edge_index = data.x, data.edge_index # First Graph Convolutional Layer x = self.conv1(x, edge_index) x = F.relu(x) x = F.dropout(x, training=self.training) # Second Graph Convolutional Layer x = self.conv2(x, edge_index) return F.log_softmax(x, dim=1) # Example usage num_features = 10 # Number of features per node num_classes = 3 # Number of classes for classification model = GCN(num_features, num_classes) This code defines a simple two-layer Graph Convolutional Network (GCN) using PyTorch and PyTorch Geometric. The model takes in the number of features per node and the number of classes for classification. Each convolutional layer (GCNConv) in the network processes the graph data, applying a graph convolution followed by a ReLU activation and dropout. Note: This is a basic example. For a real-world application, you would need to provide graph data (nodes, edges, node features) to the model and train it on a specific task like node classification, link prediction, etc.

🌐 Sources

- AssemblyAI - AI trends in 2023: Graph Neural Networks - ScienceDirect - Graph neural networks: A review of methods and applications - arXiv - Generative Graph Neural Networks for Link Prediction - YouTube - AI Explained: Graph Neural Networks and Generative AI - Medium - Top Applications of Graph Neural Networks 2021 - Towards Data Science - Applications of Graph Neural Networks - XenonStack - Graph Neural Network Applications and its Future - arXiv - Graph Neural Networks: Methods, Applications, and - neptune.ai - Graph Neural Network and Some of GNN Applications - sciencedirect.com - Graph neural networks: A review of methods and applications - frontiersin.org - Graph Neural Networks and Their Current Applications in - Jonathan Hui - Applications of Graph Neural Networks (GNN) - Medium - GNN python code in Keras and pytorch - Towards Data Science - How to Create a Graph Neural Network in Python - DataCamp - A Comprehensive Introduction to Graph Neural Networks - GitHub - Hands-On-Graph-Neural-Networks-Using-Python - Towards Data Science - Graph Neural Networks in Python - Analytics Vidhya - Getting Started with Graph Neural Networks Read the full article

[pulipuli]

好，正式確定了社會網路分析取代文字雲的分析價值。 ---- # 脈絡 / Context 最近為了分析學生在課堂的討論內容，我感覺到傳統的文字雲的上限。 文字雲終歸只能呈現出詞頻的數量，但是它不能展現出文字之間的脈絡，也就很難讓人瞭解文本中隱含的模式。 https://blog.pulipuli.info/2017/03/jieba-js-online-chinese-analyzer-jieba.html#postcatajieba-js-online-chinese-analyzer-jieba.html0_anchor4。 於是我改求助於社會網路分析。 我將斷詞後的詞彙視為節點(node)，出現在同一個段落的詞彙視為彼此連結的邊(edge)。 詞彙出現的頻率、詞彙與詞彙中連結的頻率，則作為權重設定到節點和邊上。 https://js.cytoscape.org/。 接著使用Cytoscape.js繪製社會網路分析圖，利用fcose力引導排版來繪製網路圖。 結果出乎意外地令人感到滿意。 出現頻率較高的詞彙，因為權重和邊的關係，在力引導排版下很容易出現在網路圖的中間。 加上結合權重調整的文字大小，使得出現頻率較高的詞彙更容易被看見。 這樣一來就已經達到了文字雲可以呈現的效果。 ---- # 社會網路分析 / Social network analysis。 ---- 你覺得社會網路分析跟文字探勘結合還可以怎麼用呢？靈感蒐集中！快快留言吧！。 ---- #Cytoscape #JavaScript #NetworkAnalysis 看看網頁版全文 ⇨ 不只文字雲，而是文字網路雲 / Word Network Graph https://blog.pulipuli.info/2023/02/word-network-graph.html

[emilyratajkowski164395]

AI and Network Science

AI and Network Science are increasingly converging, leveraging the power of graph-based representations to solve complex problems in interconnected systems. Network science studies the structure and dynamics of networks, such as social, biological, and technological systems. AI, particularly through Graph Neural Networks (GNNs), uses this knowledge to process and analyze graph-structured data.

Key Contributions of Network Science to AI:

Modeling Relationships: Network science provides tools to represent systems as graphs, capturing relationships and dependencies critical for AI applications.

Understanding Dynamics: Insights into network dynamics, like spreading phenomena, enhance predictive models in AI.

How AI Empowers Network Science:

Learning from Data: AI algorithms, such as GNNs, learn patterns from large-scale networks, uncovering insights in areas like social influence, disease spread, or infrastructure optimization.

Scalability: AI improves the analysis of massive, dynamic networks that traditional methods struggle with.

Applications of AI and Network Science:

Social Network Analysis: Predicting trends and detecting communities.

Biology and Healthcare: Drug discovery and modeling disease propagation.

Infrastructure Optimization: Enhancing transport or energy grids.

Knowledge Graphs: Powering natural language understanding in AI systems.

International Conference on Network Science and Graph Analytics

Our Website : https://networkscience-conferences.researchw.com/

Connected With Here :

Instagram : https://www.instagram.com/emileyvaruni/ Tumblr : https://www.tumblr.com/emileyvaruni Pinterest : https://in.pinterest.com/emileyvaruni/ Blogger : https://emileyvaruni.blogspot.com/ Twitter : https://x.com/emileyvaruni YouTube : https://www.youtube.com/@emileyvaruni

[myprogrammingsolver]

Project 4 Solved

How to run: Run NetworkAnalysis.java with the command line input of a file to be analyzed. * Compile: javac NetworkAnalysis.java * Run with command line argument: java NetworkAnalysis network_data1.txt || java NetworkAnalysis network_data2.txt * Select types of analysis on the network. * Note Choice 1: Solves the shortest path problem * Note Choice 3: Minimum spanning tree algrothim can be used…

[geekscripts]

DEDMAP: A Network Automation Framework focused on Cyber-Security | #Cybersecurity #DEDMAP #Framework #NetworkAnalysis #Security

[medsocionwheels]

Watch an overview of the 10-step process on your preferred platform!

Facebook (Not Animated, Slides only):

YouTube:

Twitter:

Instagram:

TikTok:

View the Tutorial on RPubs:

Build and Interpret a Basic Structural Topic Model in R

New R tutorial available! Follow my 10-step process for estimating and interpreting a basic structural topic model without covariates.

Preview the Tutorial With Sound (slides with commentary) @medsocionwheels Structural topic modeling: my 10 step process for estimating and interpreting a basic structural topic model without covariates in R. Full #tutorial available on medsocionwheels.com! #TopicModeling #NLP #StructuralTopicModel #QuantitativeResearch #QualitativeResearch #ResearchMethods #R #LearnR #CodingTikTok #rstats…

[expertlytics]

#datascience #datascientist #analysis #analyst #machinelearning #artificialintelligence #knowledge #networkanalysis #information #wisdom #analytics #collectiveintelligence #cognitiveanalytics (at United States) https://www.instagram.com/p/B0dxAqjgMcZ/?igshid=189v8zjqta4g3

[fiscogenova]

Il Fisco ora controllerà tutto: ecco chi sono i primi a rischio Arriva in soccorrso l'intelligenza artificiale ad aiutare l'Agenzia delle Entrate contro gli evasori fiscali: più di 300 verifiche al giorno per 3 anni!! "Non si assumeranno più fiscalisti ma ingegneri elettronici"... sarà vero? Da quando il Consiglio dei Ministri ha dato via libera al Recovery plan si vuole "salvaguardare" al meglio il patrimonio investito, 248 MILIARDI di €, contro questi "furbetti fiscali!" Chi sono i soggetti presi di mira più facilmente? Si penserebbe a chi movimenta grosse cifre di denaro senza aver fatto alcuna dichiarazione dei redditti per poi comprare beni mobili di lusso... e... invece no... I primi ad essere messi sotto controllo saranno i cittadini che hanno richiesto ed usufruito dei vari bonus erogati a seguito del blocco totale delle attività! In campo scenderanno Machine learning e e la network analysis... Macchine artificiale ok... ma che almeno le Normative siano anche più chiare... Ma anche per oggi non vi vogliamo tediare ulteriormente.. se volete ulteriori info, contattateci al 3923997758 #fiscogenova #commercialistagenova #sabrinaromano #machine learning #networkanalysis #intelligenzaartificiale #evasorifiscali #fiscofacile #evasionefiscale #agenziaentrate (presso Studio Commercialista Romano Sabrina) https://www.instagram.com/p/CPBT1zmJUds/?utm_medium=tumblr

[paulbradshaw]

As the graph below shows, mass shooting, false flag, and crisis actor conspiracy videos on YouTube are a well-established, if not flourishing genre. Across this network, one actor and its associated videos stand out above the rest. See the node directly below. (via Untrue-Tube: Monetizing Misery and Disinformation – Jonathan Albright – Medium)

[dave-columbus]

Just verified characters from Snuff, the 39th book. Nearing the end. But still needs audited by my resident Pratchett expert. Some characters listed as main aren’t that necessary to the story and will dropped from this network. Jürgen used to call that noise. . Anxious to get this into Illustrator to begin the actual graphic. Also have to finish up the roundels and start writing the text for the “How to read this chart.” . I missed doing these charts. If all my art, my strongest is my data art. . #terrypratchett #discworld #socialnetwork #networkanalysis #ankhmorpork #samvimes #vetinari #grannyweatherwax #tiffanyaching #moistvonlipwig #nodesandedges #dataart#datavis #dataviz #datavisualization #ilovecharts #ilovegraphs #centrality #eigenvector #cluster

[philearning]

Network Analysis and Synthesis - This comprehensive text on Network Analysis and Synthesis is designed for undergraduate students of Electronics and Communication Engineering, Electrical and Electronics Engineering, Electronics and Instrumentation Engineering, Electronics and Computer Engineering and Biomedical Engineering. The book will also be useful to AMIE and IETE students. Buy Now: https://bit.ly/3hfMVj3 #NetworkAnalysis #Synthesis #Electronics #Engineering #Biomedical #ComputerEngineering https://www.instagram.com/p/CBVjVLLHJ6S/?igshid=1u4kc5hz3jndl

[statrcz]

Análisis de redes en Twitter: tweets con mención a tres candidatos presidenciales en Panamá. 23 de Abril del 2019.

Utilizando el paquete SocialMediaLab de R, se realizó la extracción de los  tweets con mención a alguno de los tres candidatos analizados: @NitoCortizo, @RicardoLombanaG y @romuloroux. Los tweets utilizados, corresponden a los últimos 250 para cada candidato, antes de las 9 am del 23 de abril del 2019.

Por medio de SocialMediaLab se crearon las redes dirigidas para cada uno de los candidatos y por medio del Software Gephi se construyeron los grafos (gráficos de redes con nodos y aristas dirigidas). También con Gephi se calcularon algunas estadísticas de los grafos. Además se utilizó Excel para presentar algunas tablas.

El análisis de redes nos permite visualizar las interacciones de diferentes cuentas de Twitter, así como calcular algunas estadísticas de esas redes y también construir agrupaciones de cuentas (cluster de nodos). Esto permite determinar personas o cuentas influyentes en las redes asociadas a algunos candidatos. Las agrupaciones de cuentas además permite analizar los textos de los tweets enfocados en cada uno de los grupos y determinar “influenciadores” positivos o negativos. Esta última parte la abordaremos en un post posterior. En este analizaremos la topología de las redes y algunos estadísticas.

Para aclarar algunos términos del análisis de redes:

Los resultados son:

Se observa que la mayor cantidad de nodos la obtiene @RicardoLombanaG, seguido por @romuloroux y luego @NitoCortizo; en aristas, la mayor cantidad es para @romuloroux, seguido por @RicardoLombanaG y finalmente @NitoCortizo. 

Luego revisaremos las mediciones de Modularidad y Longitud Media del Camino.

@NitoCortizo

El gráfico para @NitoCortizo, muestra una amplia influencia de su nodo y varios sub-comunidades con nodos con menor influencia:

La red se compone de 19 nodos, algunos muy pequeños pero resaltan comunidades “lideradas” por NitoCortizo, RicardoLombanaG, tvnnoticias, DavidSaied, LRCV_Diputado, mendozajulioh27, olivaresfrias, romuloroux, juancanavarro, sembradoresdefu.

@RicardoLombanaG

La red de RicardoLombanaG, muestra una gran cohesión hacia su persona, con subcomunidades menos dominantes:

La red de RicardoLombanaG al contener mayor cantidad de nodos, genera más subcomunidades, las más relevantes: TReporta, tvnnoticias, cristal_lawson, eortega9217, bel_cer, informapty507, BlandonJose, asdrubal_ulloa.

@romuloroux

Esta red presenta diferencias con respecto a las dos anteriores: tiene un alto grado de centralidad en romuloroux, pero a la vez presenta dos nodos de gran influencia en la red: su candidato a la vicepresidencia LuisCasis20 y la cuenta de la emisora de radio Oeste985. También de destaca una fuerte presencia del nodo de NitoCortizo.

A diferencia de las redes de los otros dos candidatos, la red de romuloroux presenta dos nodos de gran relevancia: LuisCasis20, Oeste985, NitoCortizo, CaDemocratico y BlandonJose. Seguidos de otros nodos de fuerte influencia: nexnoticias, gabycarrizoj y JCasis25.

Aunque la disposición aplica para estudios por muestreo (encuestas), dado que  la Ley de Panamá exige que cualquier publicación relacionada con las próximas elecciones, aclaramos que este NO es un estudio de carácter científico.

[godigitalglobally-blog]

🤣🤣🤣 Here's your Saturday’s dose of laughs. Our favorite joke for today! 😈😈😈 When is Saturday evening and you are still in the office working and didn’t find the solution yet….and someone say that you are in a “bad mood”!!! NO! I’m not in a bad mood,…..I’m in a “Hey, if I get the opportunity to punch someone in the face today, I’m taking it” mood! And being in this mood is much more dangerous than me being in a "bad mood". #NetworkAnalysis through #PythonProgrammingLanguage and #GephiTool #GoDigitalCyprus #seocyprus https://www.instagram.com/p/Bu0KEcAlrIF/?utm_source=ig_tumblr_share&igshid=xhmcgmekoa70

[geekscripts]

WiFi_DB: Collecting and Analyzing Data from Wireless Networks | #Aircrackng #NetworkAnalysis #Wireless #Web

[atanasat]

Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis  

https://digitalpatientsafety.com/identification-of-early-warning-signals-at-the-critical-transition-point-of-colorectal-cancer-based-on-dynamic-network-analysis/  

#DHPSP #ColorectalCancer #NetworkAnalysis