# erdos-renyi

Published articles for erdos-renyi.

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## Zero-One Laws for Random Graphs

DevFeed: [Zero-One Laws for Random Graphs](<https://devfeed.tech/articles/zero-one-laws-for-random-graphs-40376.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2015/02/09/zero-one-laws-for-random-graphs/>)

Published: 2015-02-09T09:00:00Z

Content type: article

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Graphs](<https://devfeed.tech/topics/graphs.md>), [Math and Logic](<https://devfeed.tech/topics/math-and-logic.md>)

Tags: [big-o-notation](<https://devfeed.tech/tags/big-o-notation.md>), [connectivity](<https://devfeed.tech/tags/connectivity.md>), [countability](<https://devfeed.tech/tags/countability.md>), [distribution](<https://devfeed.tech/tags/distribution.md>), [erdos-renyi](<https://devfeed.tech/tags/erdos-renyi.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [logic](<https://devfeed.tech/tags/logic.md>), [logical](<https://devfeed.tech/tags/logical.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [model-theory](<https://devfeed.tech/tags/model-theory.md>), [network-science](<https://devfeed.tech/tags/network-science.md>), [parameter](<https://devfeed.tech/tags/parameter.md>), [random-graph](<https://devfeed.tech/tags/random-graph.md>), [random-graphs](<https://devfeed.tech/tags/random-graphs.md>), [statement](<https://devfeed.tech/tags/statement.md>), [vertex](<https://devfeed.tech/tags/vertex.md>)

### AI overview

This article introduces zero-one laws for Erdős-Rényi random graphs. It explains that many graph properties, including properties expressible in first-order logic, have probabilities that tend toward zero or one as the graph grows, with behavior determined by relevant thresholds or constant edge probabilities.

### Source excerpt

Last time we saw a number of properties of graphs, such as connectivity, where the probability that an Erdős-Rényi random graph $ G(n,p)$ satisfies the property is asymptotically either zero or one. And this zero or one depends on whether the parameter $ p$ is above or below a universal threshold (that depends only on $ n$ and the property in question). To remind the reader, the Erdős-Rényi random "graph" $ G(n,p)$ is a distribution over graphs that you draw from by including each edge independently with probability $ p$.

## The Giant Component and Explosive Percolation

DevFeed: [The Giant Component and Explosive Percolation](<https://devfeed.tech/articles/the-giant-component-and-explosive-percolation-40375.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2015/02/02/the-giant-component-and-explosive-percolation/>)

Published: 2015-02-02T09:00:00Z

Content type: article

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Graphs](<https://devfeed.tech/topics/graphs.md>), [Data analysis](<https://devfeed.tech/topics/data-analysis.md>)

Tags: [big-o-notation](<https://devfeed.tech/tags/big-o-notation.md>), [erdos-renyi](<https://devfeed.tech/tags/erdos-renyi.md>), [first-moment-method](<https://devfeed.tech/tags/first-moment-method.md>), [giant-component](<https://devfeed.tech/tags/giant-component.md>), [graph](<https://devfeed.tech/tags/graph.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [inequality](<https://devfeed.tech/tags/inequality.md>), [markov](<https://devfeed.tech/tags/markov.md>), [method-of-moments](<https://devfeed.tech/tags/method-of-moments.md>), [network-science](<https://devfeed.tech/tags/network-science.md>), [percolation](<https://devfeed.tech/tags/percolation.md>), [probability-theory](<https://devfeed.tech/tags/probability-theory.md>), [random-graph](<https://devfeed.tech/tags/random-graph.md>), [random-graphs](<https://devfeed.tech/tags/random-graphs.md>), [random-variables](<https://devfeed.tech/tags/random-variables.md>)

### AI overview

This article develops rigorous results about Erdős-Rényi random graphs, following a conjecture about connectivity at edge probability p = 5/n. It introduces threshold theorems and begins explaining the first moment method, Markov's inequality, and isolated vertices.

### Source excerpt

Last time we left off with a tantalizing conjecture: a random graph with edge probability $ p = 5/n$ is almost surely a connected graph. We arrived at that conjecture from some ad-hoc data analysis, so let's go back and treat it with some more rigorous mathematical techniques. As we do, we'll discover some very interesting "threshold theorems" that essentially say a random graph will either certainly have a property, or it will certainly not have it.

## Community Detection in Graphs -- a Casual Tour

DevFeed: [Community Detection in Graphs -- a Casual Tour](<https://devfeed.tech/articles/community-detection-in-graphs-a-casual-tour-40357.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2014/05/19/community-detection-in-graphs-a-casual-tour/>)

Published: 2014-05-19T10:00:32Z

Content type: article

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Graphs](<https://devfeed.tech/topics/graphs.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.md>)

Tags: [approximation](<https://devfeed.tech/tags/approximation.md>), [approximation-algorithms](<https://devfeed.tech/tags/approximation-algorithms.md>), [cliques](<https://devfeed.tech/tags/cliques.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [community-detection](<https://devfeed.tech/tags/community-detection.md>), [erdos-renyi](<https://devfeed.tech/tags/erdos-renyi.md>), [graph](<https://devfeed.tech/tags/graph.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [modularity](<https://devfeed.tech/tags/modularity.md>), [network](<https://devfeed.tech/tags/network.md>), [newman](<https://devfeed.tech/tags/newman.md>), [np-hard](<https://devfeed.tech/tags/np-hard.md>), [power-law-distribution](<https://devfeed.tech/tags/power-law-distribution.md>), [random-graph](<https://devfeed.tech/tags/random-graph.md>), [randomized-algorithm](<https://devfeed.tech/tags/randomized-algorithm.md>), [technical](<https://devfeed.tech/tags/technical.md>), [walktrap](<https://devfeed.tech/tags/walktrap.md>)

### AI overview

This introductory article examines community detection in graphs. It explains the informal idea of a community, why defining one precisely and usefully is difficult, and how the clique-based approach leads to computationally intractable problems, including the NP-hardness of finding the largest clique.

### Source excerpt

Graphs are among the most interesting and useful objects in mathematics. Any situation or idea that can be described by objects with connections is a graph, and one of the most prominent examples of a real-world graph that one can come up with is a social network. Recall, if you aren't already familiar with this blog's gentle introduction to graphs, that a graph $ G$ is defined by a set of vertices $ V$, and a set of edges $ E$, each of which connects two vertices.