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Re: [igraph] igraph-help Digest, Vol 129, Issue 17

From: Perrone, Alexander G.
Subject: Re: [igraph] igraph-help Digest, Vol 129, Issue 17
Date: Mon, 29 May 2017 03:04:12 +0000

Lookman SANNI, 

Subgraphs like k-core or k-truss essentially classify the edges. You might want 
to look at k-truss which is not in igraph. It's on my github. Basically, it 
clusters edges according to how many triangles each edge is in. Depending on 
what you're looking for out of clustering of edges, it's an option. 

Alex Perrone

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Subject: igraph-help Digest, Vol 129, Issue 17

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Today's Topics:

   1. Re: Graph Clustering (Tamas Nepusz)


Message: 1
Date: Sat, 27 May 2017 21:16:43 +0200
From: Tamas Nepusz <address@hidden>
To: Help for igraph users <address@hidden>
Subject: Re: [igraph] Graph Clustering
Content-Type: text/plain; charset="utf-8"


A common trick is to construct the line graph of the original graph, do the
clustering on the line graph (where each node corresponds to a single edge
from the original graph), and then map the obtained clustering back to the
edges of the original graph.


On Fri, May 26, 2017 at 10:53 PM, lookman sanni <address@hidden>

> Hi all,
> I am currently investigating graph clustering techniques/algorithms for
> the purpose of anomaly detection in static, edge attributed and
> disconnected graphs.
> From what I have seen so far, most of the graph clustering algorithms for
> anomaly detection output either a binary *node *classification or a *node
> *anomaly score.
> To the best of your knowledge, is there any algorithm rather providing
> either a binary *edge* classification or an *edge* anomaly score ?
> Thank you.
> --
> Lookman SANNI
> _______________________________________________
> igraph-help mailing list
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> https://lists.nongnu.org/mailman/listinfo/igraph-help
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