The graph is the conceptual basis for web3 networks. This concept will be familiar to those who have studied connectivism, as the idea of connectivism is that knowledge consists of the relations between nodes in a network - in other words, that knowledge is a graph (and not, say, a sequence of facts and instructions).
Activities
2018/11/08 12:00 Conversation with Ben WerdmullerSynopsis
The graph is the conceptual basis for web3 networks. This concept will be familiar to those who have studied connectivism, as the idea of connectivism is that knowledge consists of the relations between nodes in a network - in other words, that knowledge is a graph (and not, say, a sequence of facts and instructions).
Graphs, and especially dynamic graphs, have special properties, the results of which can be found in social network theory, in modern artificial intelligence, and in economic and political theory.
Previous work in graphs on the internet have had to do with the semantics of graphs; hence, for example, we say the development of things like the semantic web and the web of trust. These have been limited successes. In web3 the connections between nodes (the "edges") are created using cryptography, thus creating chains or trees with incorruptible connections.
One example of this is the Merkle Tree, where branches contain hashes of the leaves, and trunks contain hashes of the branches. Graphs - such as the directional acyclic graph (DAG) - can be created in this manner.
The data structures we can build using these technologies have created a new type of content. One well-known example is BitCoin, which is based on the recording of transactions in a blockchain, which is essentially a has chain. Another example is the collection of updated versions of software stored in GitHub, which manages version control and software replication using DAGs. Attribution networks, conceptual networks, websites - all these can be represented using graphs.
In connectivism we have explored the idea of thinking of knowledge as a graph, and of learning as the growth and manipulation of a graph. It helps learners understand that each idea connects to another, and it's not the individual idea that's important, but rather how the entire graph grows and develops.
It helps us see how a graph - and hence, knowledge - is not merely a representational system, but is rather a perceptual system, where the graph is not merely the repository, but a growing and dynamic entity shaped by - and shaping - the environment around itself.
Graphs and graph theory demonstrate in a concrete way how everything depends on something else, and helps us place our understanding of ourselves, or knowledge, and our work into a wider context. Hash graphs take this a step further by illustrating fundamental knowledge-creation mechanisms as cloning, forking, versioning and merging.
Tasks
Create a Model GraphThis week's task has three parts
- Create a model graph of some aspect of the E-Learning 3.0 course (it doesn't have to be an actual graph, only a representation of what an actual graph might look like. We've already seen, eg., graphs on the relations between people in the course. Could there be other types of graphs?
- In your model, consider how the states of the entities in that graph might vary. Consider not only how nodes might vary (eg., a person might have a different height over time) but also how the edges might vary (eg., a person might have a different strength of relation (calculated how?) with another person over time).
- In your model, consider how knowledge about the changes in states in the graph might be used.
Record the results in your blog or website. And share with #el30.
Due: Nov 21, 2024Media
Conversation with Ben Werdmuller Nov 08, 2018 video Now working with Unlock, Ben Werdmuller co-founded Elgg and Known, worked on Medium and Latakoo, and invested in innovative media startups to support a stronger democracy at Matter. We talked about blockchain, decentralized applications, indieweb, and how people can have their own online presence.
E-Learning 3.0 - Graph Nov 10, 2018 video The graph is the conceptual basis for web3 networks. A graph is a distributed representation of a state of affairs created by our interactions with each other. The graph is at once the outcome of these interactions and the source of truth about those states of affairs.
Resources
Feature Article E-Learning 3.0, Part 3: Graphstephen@downes.ca, Nov 10, 2018.
The graph is the conceptual basis for web3 networks. A graph is a distributed representation of a state of affairs created by our interactions with each other. The graph is at once the outcome of these interactions and the source of truth about those states of affairs. The graph, properly constructed, is not merely a knowledge repository, but a perceptual system that draws on the individual experiences and contributions of each node. This informs not only what we learn, but how we learn.
2018/11/08
A collaborative social publishing engine. Known allows any number of users to post to a shared site with blog posts, status updates, photographs, and more. Its robust open source framework can be used to build fully-fledged community sites, or a blog for a single user.
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This post, referenced during our conversation today, references the case of Gab and raises the issue of whether the decentralized web is a haven for objectionable content. It's also a good example of a website using WebMentions to link to comments made on other website (including, of everything works, this one).
Web: [Direct Link] [This Post]Alexander Grech, Anthony F. Camilleri, Joint Research Centre, European Commission, 2018/11/06
This is a long (136 page PDF) and detailed report on blockchains in education. The authors work slowly and deliberately in their pursuit of accuracy and clarity, which results in a presentation that will be easily understood by most readers. There is a wealth of examples in the document describing use cases, scenarios and pilot projects, and companies involved in the space.
Web: [Direct Link] [This Post]Brant Carson, Matt Higginson, Simon London, McKinsey, 2018/11/06
This podcast transcript provides a level-headed overview of blockchain technologies focusing especially on the trade-offs the use of blockchain entails (for example: less efficient databases in exchange for immutability). There's also a nice table depicting the major use cases for blockchain. And there's a nice look at the different motivations for employing blockchain.
Web: [Direct Link] [This Post]Peter Mell, Nik Roby, Karen Scarfone, Dylan Yaga, National Institute of Standards and Technology, 2018/11/06
This is a good crisp summary that doesn't shy away from technical detail but steps through the major elements of blockchain technology with clarity and precision. The sections on blockchain components (section 3) and consensus models (section 4) are particularly strong. It even comes with a fun blockchain use case flowchart.
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You may have heard of GitHub - the open source software repository that was recently acquired by Microsoft for $7.5 billion. GitHub is important because it allows authors to release related versions of their software, to incorporate and merge contributions from many authors, and to allow people to create their own version (or 'fork') any application. To do this, GitHub is structured as a Directed Acyclic Graph, creating a series of relationships among code libraries.
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Graphs are important types of data structures. Instead of thinking of things in rows and columns (the way we would in a spreadsheet or a database) we think of things as nodes and edges. This page has a very brief description of a graph data structure and then a long list of things that can be done with graphs - cycling, sortinfg, spanning, searching. This page is meant to explor, not to learn - ollow the links, try running some of the code (click on the r'run in IDE button').
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The diagram in this resource descibes some different types of neural networks. Take a look at the specific tasks they perform - neural networks are good at things like classification and recgnition, as well as regression (that is, finding a trend or regulrity in data). I got this image from this page, which has more resourcs on neural networks.
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Neural Networks are types of graphs. In the past I have stated that in order to be a network, a change of state in one entity in a graph must be capable of producing a change of state in another entity. Neural networks are therefore dynamic and interactive graphs. This resource describes a bunch of different neural networks. Different neural networks have different capabilities, and today are playing an increasingly important role in artificial intelligence.
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This is a gentle introduction to graph theory. Graphs are data structires in which entities - called 'nodes' - are connected to other entitis via some sort of a link - called an 'edge'. In graph theory there are no limits on what can be connected, nor how they can be connected. Defining graphs in specific ways, however, creates the structures that underlie most of the modern web.
Web: [Direct Link] [This Post]Posts
This week in the #EL30 course with Stephen Downes, we are looking at graphs. First, two passages from his recent draft monograph on graphs. “In connectivism we have explored the idea of thinking of knowledge as a graph, and of learning as the growth and manipulation of a graph. It helps learners understand that each […]
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It bugs me when some peer nerds urge all the rest of the world to adopt our way of thinking. Probably they are confusing two important things.
Continue reading →
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Graph is the Topic for Week 3 of Stephen Downes’ E-Learning 3.0 MOOC. Again, he has provided a good Synopsis – see https://el30.mooc.ca/cgi-bin/page.cgi?module=7. In the last three paragraphs in this synopsis he writes: In connectivism we have explored the idea… Continue reading →
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I think I get the idea of AI and neural networks and that a graph is an abstraction of human functions like the zoo of neural networks described in The Neural Network Zoo . However, I was a bit stumped with the week’s task to create a model graph besides the social network graph that Stephen … Continue reading "E-Lerning 3.0 Week 3 Model Graph Task"
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Week 3 of Stephen Downes’ E-Learning 3.0 course is looking at Graphs. Stephen recommended some resources for this topic. These included: Vaidehi Joshi’s (2017) gentle introduction to graph theory. In her discussion of graphs, Vaidehi observes “in mathematics, graphs are a way to formally represent a network, which is basically just a collection of objects … Continue reading #EL30 Graphing
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This is an important article by Ton Zijlstra about “distributed technology” and the long tail. In fact, he applies it to Mastodon, a decentralized combination of microblogging and virtual communities. I quote: This is the notion that tool usage having a long tail is a measure of distribution, and as such a proxy for networked […]
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The guest for this third week of Stephen Downes’ E-Learning 3.0 MOOC was Ben Werdmuller. I took a screenshot from the Hangout, because he has such a winning smile. Just what you need for a difficult topic! These weekly conversations… Continue reading →
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If you want to try it yourself, here is my stuff.
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My task for you involves some concepts from the synopsis texts, and you should connect and annotate them.
Continue reading →
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Stephen has tasked us all with creating a graph of some sort for #el30 this week. Questions that came into my mind were: What are the parameters? How do they interact? How can I make visible the potentials? I’m thinking of learning and what’s visible, what we bring, resources, and what is ideal to implement […]
The post Graph #el30 Week 3 appeared first on lauraritchie.com.
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The task for this week in the course E-learning 3.0: Create a model graph of some aspect of the E-Learning 3.0 course (it doesn’t have to be an actual graph, only a representation of what an actual graph might look like. We’ve already seen, eg., graphs on the relations between people in the course. Could […]
Web: [Direct Link] [This Post]
I think I get the idea of AI and neural networks and that a graph is an abstraction of human functions like the zoo of neural networks described in The Neural Network Zoo . However, I was a bit stumped with the week’s task to create a model graph besides the social network graph that Stephen … Continue reading "E-Lerning 3.0 Week 3 Model Graph Task"
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Facilitator Stephen Downes of the course E-learning 3.0 (#el30) explains Graphs in this video. In his own words: The graph is the conceptual basis for web3 networks. A graph is a distributed representation of a state of affairs created by our interactions with each other. The graph is at once the outcome of these interactions […]
Web: [Direct Link] [This Post]
In the course E-learning 3.0 our facilitator Stephen Downes had an interview with Ben Werdmuller who co-founded Elgg and Known, worked on Medium and Latakoo, and invested in innovative media startups to support a stronger democracy at Matter. These ventures are very related to all things decentralized web, the movement away from the big silos […]
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I’m sure I’m not alone in hearing the word ‘graph’ and thinking about high school maths or statistics. But, in the context of this week’s topic on the eLearning 3.0 MOOC, the meaning is totally different. In this context, when … Continue reading →
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I watched a video today for our eLearning 3.0 class – an interview between Stephen Downes (course facilitator) and Ben Werdmüller, cofounder of ELGG and Known. This was an interesting conversation for myself because I am very interested in the … Continue reading →
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ioannouolga, connecting data to information to knowledge, 2018/11/13
Trees start from a root node and might connect to other nodes, which means that could contain subtrees within them. Trees are defined by a certain set of rules: one root node may or may not connect to others, but ultimately, it all stems from one specific place. The tree follows one direction and cannot […]
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For #EL30 this week, the topic of Graph is explored. This blog post will not address our task for this week but will instead capture some of what I’ve been considering about the topic and some excellent resources I’ve found that have helped to shape my thoughts. The graph (think network, community, ecosystem of connections) is seen as … Continue reading "#EL30 Week 3 – Graph"
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This is a response to the E-Learning 3.0 task  for course participants created by Matthias Melcher. See https://x28newblog.wordpress.com/2018/11/09/el30-graph-task/ The task requires that we select from one of the topics of this course, and create a map from the list of… Continue reading →
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dogtrax, EL30 – Kevin's Meandering Mind, 2018/11/13
I am reading and re-reading Stephen Downes’ piece on graphs for the EL30 (E-Learning 3.0) course, pondering the ways he thinks about the representation of social networks, stories and more. I am not even sure I follow it all. I’m fine with that, for it has me thinking in differen directions. What I am wondering […]
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Found via Twitter, definitely worth a look: "The graph is directed. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm. The graph was laid out using the Grid layout algorithm."
Web: [Direct Link] [This Post]noreply@blogger.com (Apostolos K. ("AK")), Multilitteratus Incognitus, 2018/11/15
Attempt at witty title probably failed :-) I guess I am a little rusty with creating meaningful blog titles since I have not been blogging frequently recently. Oh well. I will get back into the swing of things once I finish my EdD...or not... ;-) In any case, I am catching up with #el30, more specifically last week's guest Ben Werdmuller (see recording here). Interesting fun fact - Ben is the creator of Elgg, which is the platform that Athabasca University's "Landing" runs on. There were quite a few interesting things that came out of the conversation but there were two that really stuck out to me.
Web: [Direct Link] [This Post] The graph refers to the formal properties network and the network to the physical properties of the graph. The beginning of the Web: It started out as a decentralized network of interconnected servers, overtime it became dominated by platforms like google. The original web was hard to use. They needed an index, bookmarks etc. The people […]
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