E-Learning-Inclusivo (Mashup)
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E-Learning-Inclusivo (Mashup)
Aprendizaje con TIC basado en los aprendices.
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How To Detect #Communities Using Social Network Analysis | #SNA

How To Detect #Communities Using Social Network Analysis | #SNA | E-Learning-Inclusivo (Mashup) | Scoop.it

Via luiy
luiy's curator insight, September 16, 2014 10:45 AM

Think of communities as very similar to the segments identified in a brand’s customer segmentation model. (With demographics analysis layered on, you might even find that they’re the same.)

While direct marketing communications is often customized by segment, historically this hasn’t been something brands have done in social. But, using social network analysis and also Twitter & Facebook ad targeting, it’s possible to send specific messages to specific groups of people.

 

Powered by Pulsar TRAC these could be people engaging in a specific conversation, individuals sharing a piece of content online, or the followers of an account on Twitter. Any group of people, in essence, as long as we can define that audience through some property of its behaviour in social media – such as keyword, user bio, or location.

 

Community analysis allows brands to really understand the behavior of their audiences in a way they can’t replicate with offline, non-social data.

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A smart local moving #algorithm for large-scale modularity-based community detection | #SNA #clustering

A smart local moving #algorithm for large-scale modularity-based community detection | #SNA #clustering | E-Learning-Inclusivo (Mashup) | Scoop.it

Via luiy
luiy's curator insight, November 15, 2013 10:41 AM

Our smart local moving (SLM) algorithm is an algorithm for community detection (or clustering) in large networks. The SLM algorithm maximizes a so-called modularity function. The algorithm has been successfully applied to networks with tens of millions of nodes and hundreds of millions of edges. The details of the algorithm are documented in a paper (preprint available here).

 

The SLM algorithm has been implemented in the Modularity Optimizer, a simple command-line computer program written in Java. The Modularity Optimizer can be freely downloaded. The program can be run on any system that supports Java version 1.6 or higher. In addition to the SLM algorithm, the Modularity Optimizer also provides an implementation of the well-known Louvain algorithm for large-scale community detection developed by Vincent Blondel and colleagues. An extension of the Louvain algorithm with a multilevel refinement procedure, as proposed by Randolf Rotta and Andreas Noack, is implemented as well. All algorithms implemented in the Modularity Optimizer support the use of a resolution parameter to determine the granularity level at which communities are detected.

Jean-Michel Livowsky's curator insight, November 16, 2013 8:38 AM

SLM algoritm. Very nice move in this complex approach of collective intelligence.

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graph-tool: Efficent network analysis with #python | #SNA #tools

graph-tool: Efficent network analysis with #python | #SNA #tools | E-Learning-Inclusivo (Mashup) | Scoop.it
graph-tool: Efficent network analysis with python

Via luiy
luiy's curator insight, September 11, 2014 10:29 AM

An extensive array of features is included, such as support for arbitrary vertex, edge or graph properties, efficient "on the fly" filtering of vertices and edges, powerful graph I/O using the GraphML, GML and dot file formats, graph pickling, graph statistics (degree/property histogram, vertex correlations, average shortest distance, etc.), centrality measures, standard topological algorithms (isomorphism, minimum spanning tree, connected components, dominator tree, maximum flow, etc.), generation of random graphs with arbitrary degrees and correlations, detection of modules and communities via statistical inference ,,,,,,