Slide deck from my presentation at NYC's #Pydata 2012 conference - http://nyc2012.pydata.org/abstracts/#gephi
Talk abstract:
Are you interested in working with social data to map out communities and connections between friends, fans and followers? In this session I'll show ways in which we use the python networkx library along with the open source gephi visualization tool to make sense of social network data. We'll take a few examples from Twitter, look at how a hashtag spreads through the network, and then analyze the connections between users posting to the hashtag. We'll be constructing graphs, running stats on them and then visualizing the output.
Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new pos- sibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, fil- tering, navigating, manipulating and clustering
Prepared as a conference tutorial, MIC-Electrical, Athens, Greece, 5th April 2014, updated and delivered again in Beijing, China, 27 January 2015 to students from Complex Systems Group, CSRC and Dept. of Engineering Physics, Tsinghua University
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new pos- sibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, fil- tering, navigating, manipulating and clustering
Prepared as a conference tutorial, MIC-Electrical, Athens, Greece, 5th April 2014, updated and delivered again in Beijing, China, 27 January 2015 to students from Complex Systems Group, CSRC and Dept. of Engineering Physics, Tsinghua University
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
Digital Ethnography: New Ways of Knowing Ourselves and Our CultureRuss Nelson
Presentation given at the Web 2.0 Expo in New York on Nov 11, 2009.
Online advertisers and web analysts are awash in a sea of data: pageviews, CPMs sold, subscriptions, sentiment, friends, fans, click-through rates, comments, posts, re-tweets… These metrics are great at identifying the “Who?” and “What?” of online behavior but they often leave out the “How?”, “Where?”, and “Why?”.
Unlike traditional market research, ethnography uses observation to focus on what people do, not on what they say they do. Ethnography communicates a social story, pulling the audience into the daily lives of the respondents. Despite the introduction of new technologies like social media, humans are still telling the same, vivid stories, just in different ways.
Ethics in Data Science and Machine LearningHJ van Veen
Introduction and overview on ethics in data science and machine learning, variations and examples of algorithmic bias, and a call-to-action for self-regulation. Given by Thierry Silbermann as part of the Sao Paulo Machine Learning Meetup, theme: "Ethics".
https://www.linkedin.com/in/thierrysilbermann
https://twitter.com/silbermannt
https://github.com/thierry-silbermann
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
This slide deck gives a general overview of Data Visualization, with inspiring examples, the strength and weaknesses of the human visual system, a few technical frameworks that may be used for creating your own visualizations and some design concepts from the data visualization field.
An introductory-to-mid level to presentation to complex network analysis: network metrics, analysis of online social networks, approximated algorithms, memorization issues, storage.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
Digital Ethnography: New Ways of Knowing Ourselves and Our CultureRuss Nelson
Presentation given at the Web 2.0 Expo in New York on Nov 11, 2009.
Online advertisers and web analysts are awash in a sea of data: pageviews, CPMs sold, subscriptions, sentiment, friends, fans, click-through rates, comments, posts, re-tweets… These metrics are great at identifying the “Who?” and “What?” of online behavior but they often leave out the “How?”, “Where?”, and “Why?”.
Unlike traditional market research, ethnography uses observation to focus on what people do, not on what they say they do. Ethnography communicates a social story, pulling the audience into the daily lives of the respondents. Despite the introduction of new technologies like social media, humans are still telling the same, vivid stories, just in different ways.
Ethics in Data Science and Machine LearningHJ van Veen
Introduction and overview on ethics in data science and machine learning, variations and examples of algorithmic bias, and a call-to-action for self-regulation. Given by Thierry Silbermann as part of the Sao Paulo Machine Learning Meetup, theme: "Ethics".
https://www.linkedin.com/in/thierrysilbermann
https://twitter.com/silbermannt
https://github.com/thierry-silbermann
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
This slide deck gives a general overview of Data Visualization, with inspiring examples, the strength and weaknesses of the human visual system, a few technical frameworks that may be used for creating your own visualizations and some design concepts from the data visualization field.
An introductory-to-mid level to presentation to complex network analysis: network metrics, analysis of online social networks, approximated algorithms, memorization issues, storage.
[20140830, Pycon2014] NetworkX를 이용한 네트워크 분석Kyunghoon Kim
UNIST(유니스트)
NaturalScience Mathematical Sciences Kyunghoon Kim
자연과학부 수리과학과 김경훈
기본적인 네트워크 분석
Python Library NetworkX Tutorial Korean Version
http://www.pycon.kr/2014/program/7
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A Fast and Dirty Intro to NetworkX (and D3)Lynn Cherny
Using the python lib NetworkX to calculate stats on a Twitter network, and then display the results in several D3.js visualizations. Links to demos and source files. I'm @arnicas and live at www.ghostweather.com.
The Slashdot Zoo: Mining a Social Network with Negative EdgesJérôme KUNEGIS
We analyse the corpus of user relationships of the Slashdot technology news site. The data was collected from the Slashdot Zoo feature where users of the website can tag other users as friends and foes, providing positive and negative endorsements. We adapt social network analysis techniques to the problem of negative edge weights. In particular, we consider signed variants of global network characteristics such as
the clustering coefficient, node-level characteristics such as centrality and popularity measures, and link-level characteristics such as distances and similarity measures. We evaluate
these measures on the task of identifying unpopular users,
as well as on the task of predicting the sign of links and show that the network exhibits multiplicative transitivity which allows algebraic methods based on matrix multiplication to
be used. We compare our methods to traditional methods which are only suitable for positively weighted edges.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
2010 june - personal democracy forum - marc smith - mapping political socia...Marc Smith
Marc Smith's presentation to the Personal Democracy Forum 2010 in New York City on June 4th, 2010 about the use of NodeXL, a social media network analysis tool, to map political topics in services like Twitter.
NodeXL is available from http://nodexl.codeplex.com
This talk combines two stories about the analysis of data associated with diseases. In the first, we introduce community detection in networks and use network representations of genetic virulence factor similarities between different uropathogenic E. coli strains to identify communities of these strains that are more similar to each other than to the rest of the studied population. We then discuss the clinical differences between these E. coli communities. In the second story, we investigate metabolomic data obtained from stool samples of hospitalized patients. We employ a variety of methods for handling this sparse data to generate a new classifier for the presence of C.difficile in the samples. Working closely with our clinical collaborators, we then obtain a wholly new and surprisingly simple and accurate measurement for detecting the presence of active C. difficile infections.
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
2013 NodeXL Social Media Network AnalysisMarc Smith
Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
Finding Key Influencers and Viral Topics in Twitter Networks Related to ISIS,...Steve Kramer
Paragon Science used a combination of network analysis, community detection, topic detection, sentiment analysis, and anomaly detection methods to find key influencers and viral topics in two recent Twitter data sets: one of 7.9 M tweets regarding ISIS, a second set consisting of more than 117 M tweets about the 2016 primary elections, and a third set of 7M tweets realted to Brexit.
Paragon Science's patented dynamic anomaly detection technology is based on methods drawn from dynamical systems and chaos theory. In particular, we can calculate finite-time Lyapunov exponents from any time-dependent data stream to find the clusters of entities that are behaving most chaotically compared to the rest of the data set. Because we do not have to specify normal vs. abnormal behavior in advance, no machine learning per se is required. In a robust fashion that is tolerant of missing or erroneous data, we can identify the "unknown unknowns" that can represent threats to be mitigate or opportunities to be seized. To date, our technique has been applied successfully to a broad range of industry verticals, including healthcare data (Advisory Board Company), web user behavior data (Vast), mobile phone data (Place IQ), vehicle pricing analytics (Digital Motorworks/CDK Global), online coupon data (RetailMeNot), email monitoring for patent law cases, and social media monitoring.
Algorithms can be very useful, especially when filtering through lots and lots of information. They can help us identify trends, quickly sum up popular concepts, and detect outliers at very early stages. I'd like to believe that my role as a data scientist is to give the digg editors superpowers - help them find quality content as early as possible.
With the growing amount of information/content published, and people’s continued expectation that “news will find them”, there's no doubt we need to depend on algorithmic mechanisms. But we also need to design these algorithmic systems while questioning ourselves – what is it that we’re optimizing? Is it only “eyeballs” or is there a higher cause we should aim for?
The choices that we make when designing these systems are like setting the rules of a game: they determine who wins and who loses.
A Networked Take on Influence: what we learn from dataGilad Lotan
Analyzing data from social interactions on Twitter and Facebook gives us clues as to how people choose to manage their attention. When intelligently predicted, captured and visualized, we witness emerging patterns. Some swear by a certain time of day. Others spend an inordinate amount of time trying to reach certain users deemed "influencers." But its clear that the networked dynamics at play are much more complicated. While there is no simple recipe for predicting information flows in online social spaces, there's plenty of room for optimization.
Networked Audiences: what we learn from data / Gilad Lotan / IPZ2012Gilad Lotan
At a time when attention is a scarce commodity, true power lies in understanding how information flows within networked audiences. It is no longer possible to demand one's attention, or even expect it at a certain point in time. For a message to spread, it must be picked out from overflowing streams of updates, photos and links, and chosen to be reposted by each individual. The networked nature of social media may give some messages an overwhelming boost in popularity, but in most cases they fade as fast as they were created. It is imperative that we use available data to better model, track and gain insight about our audiences in order to make the best decision at any given time.
Whether you're a multi-national enterprise, a local deli or a mah-jong meetup, the proliferation of social network services like Twitter have created an expectation that you interact with your customers, users and followers. There's an expectation to connect rather than broadcast. We've been hearing this over and over this morning - you are a brand. And as a brand you are expected to interact with your audience like a person would interact with others. You need to engage in conversations, provide and receive feedback, network, create hype, and do all this in a timely manner.
But how can we be expected to interact with an ever growing and diverse group of people when we can't really "see" them?
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
9. • Node network properties
– from immediate connections
indegree=3
• indegree
how many directed edges (arcs) are incident on a node
outdegree=2
• outdegree
how many directed edges (arcs) originate at a node
degree=5
• degree (in or out)
number of edges incident on a node
– from the entire graph
• centrality (betweenness, closeness)
Source: Lada Adamic (SI508-F08)
10. Example Graph Types
• Complete Graph
• Bipartite Graph
– Vertices can be divided into two disjoint sets
– Ex: students & schools
11.
12. Social Network Attributes
• Scale Free
– Degree distribution follows a power law
– Barabasi et al (‘99): mapped the topology of a portion
of the web
• Small World
– Most nodes are not neighbors, but can be reached by
small number of hops
– Watts & Strogatz (’98)
– Properties: cliques, sub networks with high clustering
coefficient, most pairs of nodes connected by at least
one short path
13. (Zachary) Karate club graph
social network of friendships
between 34 members of a karate
club at a US university in the
1970s.
Standard test network for
clustering algorithms -> during
the observation period the club
broke up into two separate clubs
over a conflict.
15. Graph Layout
• Open Ord
– Better distinguishes clusters
• Yifan Hu
• Force Atlas
• Fruchterman Reingold
– Graph as a system of mass particles
(nodes:particles, edges:springs)
21. Twitter Users with Python in their Bios
• 2 days of Twitter data (Oct 24th and 25th)
• Total: 4246 users (62k tweets)
• @mikanyan1 tweeted 795 times
27. Thank You
Gilad Lotan
Twitter: @gilgul
Github: giladlotan
Editor's Notes
Homophily
Endogenous Trend – information spread
Exogenous information spread
Hashtags have emerged as a way for people to gather around topics or events.
- Mitt romney: #gayrights, #lgbt, #jesus, #flipflop, #jobs, #economy- Newt Gingrich: #palestine, #OWS, #immigration, #abortion (he famously said – “Stop whining, take a bath and get a job!”Equal: #republican, #dems, #economics, #amnestyCo-occurence
Networkx supports
Zachary's Karate Club Graph describes the friendships between the members of a US karate club in the 1970s. The significant feature of this social network is that the club president and the instructor were involved in a dispute (some might say: a fight) over the issue of how much to charge for lessons. This split the club into two factions, one centred around the president, and the other centred around the instructor.
Betweenness – number of shortest paths from all vertices that pass through that node / positioningCloseness – how fast it will take to spread information from s to all other nodes sequentially / distance of s from all other actors in a networkEigenvector – measure of the influence of a node (page rank, connections to high scoring nodes contribute more to the score)Clustering Coefficient – measure of degree to which nodes in a graph tend to cluster together (how close to being a clique = 1)
NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and function of complex networks.NetworkX was born in May 2002. The original version was designed and written by AricHagberg, Dan Schult, and Pieter Swart in 2002 and 2003. The first public release was in April 2005.