# community detection

Published articles for community detection.

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## 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.