How the Fast Unfolding Algorithm Detects Communities in Large Networks

The digital world has opened up a totally different way of creating relationships. It’s also unleashed an ocean of data we can analyze to get a better understanding of human behavior.

Social media data refers to all of the raw insights and information collected from an individual’s social media activity. We can create networks from these social media activities to get a better perception of that individual.

These networks can range widely and might include your Facebook friends, the products you recently purchased on Amazon, the tweets you liked or retweeted, your favorite food you ordered from Zomato, the search you made on Google, or the image you recently liked on Instagram.

Companies use these networks to classify their users into different groups. This helps them

• do market research
• generate leads
• better serve their customers
• find and share photos and videos
• discover and discuss trending content
• share information about services and restaurants
• connect with others around a shared interest or hobby
• and more.

The list is pretty much endless.

Before we get too into the weeds, let’s quickly break down the distinction between different components of a network.

The Network

A network is a web of interconnected personal relationships. For example, different individuals can communicate with one another in a social media group through a dynamic web of relationships.

A network is comprised of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them.

#social-media #network-analysis #community-detection #social-network-analysis #fast-unfolding-algorithm

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How the Fast Unfolding Algorithm Detects Communities in Large Networks

The digital world has opened up a totally different way of creating relationships. It’s also unleashed an ocean of data we can analyze to get a better understanding of human behavior.

Social media data refers to all of the raw insights and information collected from an individual’s social media activity. We can create networks from these social media activities to get a better perception of that individual.

These networks can range widely and might include your Facebook friends, the products you recently purchased on Amazon, the tweets you liked or retweeted, your favorite food you ordered from Zomato, the search you made on Google, or the image you recently liked on Instagram.

Companies use these networks to classify their users into different groups. This helps them

• do market research
• generate leads
• better serve their customers
• find and share photos and videos
• discover and discuss trending content
• share information about services and restaurants
• connect with others around a shared interest or hobby
• and more.

The list is pretty much endless.

Before we get too into the weeds, let’s quickly break down the distinction between different components of a network.

The Network

A network is a web of interconnected personal relationships. For example, different individuals can communicate with one another in a social media group through a dynamic web of relationships.

A network is comprised of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them.

#social-media #network-analysis #community-detection #social-network-analysis #fast-unfolding-algorithm

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.

Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

• Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
• **Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
• **Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
• **Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
• Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
• **Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
• **Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
• **Self-Report: **Automatically communicate with its environment and exchange necessary information.
• Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

• software-defined networking (SDN) control
• industry-standard models and open APIs
• Real-time analytics/telemetry
• big data processing
• cross-domain orchestration
• programmable infrastructure
• cloud-native virtualized network functions (VNF)
• DevOps agile development process
• everything-as-service design paradigm
• intelligent process automation
• edge computing
• cloud infrastructure
• programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
• open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks

Matic Network in Blockchain Gaming

Matic Network is getting lots of attraction amidst the blockchain game developers. This is because, their competition has stepped away from the gaming scene. Matic - as a general purpose platform, capable of creating all types of DApps, and have already build 60+ DApps on Matic Network.

As a result Matic Network is busy gaining a lots of new gaming partners. They have already been integrated into many gaming DApps.

Key reasons why DApps chooses Matic Network

• Near-instant blockchain transactions
• Low Transaction fees >> less than 1/1000th of the fees on the Ethereum mainchain
• Seamless migration for existing Ethereum DApps
• Access to, and assistance with, a wide range of developer tooling.
• Unparalleled technical support for developers.

If you have an idea to build your own Gaming DApp - you could benefit from matic network’s high-speed, low-fee infrastructure and our assistance to transform your DApp from a great idea into a successful DApp business.

Being a Predominant DApp Game Development Company, GamesDApp helps you to Build DApp Game on matic network and also expertize in developing various popular games on the blockchain network using smart contract.

Hire Blockchain Game Developers >> https://www.gamesd.app/#contactus

#matic network #build dapp game on matic network #dapp game on matic network #matic network in blockchain gaming #matic network for game development

Connecting with the Docker Community– Recap of Our First Community All Hands

Last week, we held our first Community All Hands and the response was phenomenal. A huge thank you to all 1,100+ people who joined. If you missed it, you can watch the recording here. You can also find answers to those questions that came in towards the end that we didn’t have time to answer here.

This all-hands was an effort to further deepen our engagement with the community and bring users, contributors and staff together on a quarterly basis to share updates on what we’re working on and what our priorities are for 2021 and beyond. The event was also an opportunity to give the community direct access to Docker’s leadership and provide a platform to submit questions and upvote those that are most relevant and important to people.

The overwhelming piece of feedback we got from attendees was that the event was too short and people would have loved to see more demos. We certainly had a packed agenda and we did our best to squeeze in as much into an hour. For our next one (in February 2021!), we’ll aim to extend the event by 30 minutes and include more live demos. We’ll also try to make it more interactive and give additional time to answer more questions. If you have any other ideas on how we can improve the all-hands and make it more engaging, don’t hesitate to send me a note on our community slack (@William).

Community events are a key pillar of our community-building strategy and we look forward to experimenting with new types of events like this one to continue pushing for more participation, openness and engagement. Onwards!

#community #community #developers #docker #docker community

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