Print

Print


*****  To join INSNA, visit http://www.insna.org  *****

or use Tulip (opensource, scalable, customizable)

http://tulip.labri.fr

--

Le 07/06/2012 04:01, Michele Dallachiesa a écrit :
> *****  To join INSNA, visit http://www.insna.org  *****
>
> Hi Hyokun,
> You can try to visualize this huge network with one of these tools:
>
> * Pajek - http://pajek.imfm.si/doku.php
> * GraphViz - http://www.graphviz.org
> * Cytoscape - www.cytoscape.org
> * Gephi - http://gephi.org
> * GraphInsight - http://www.graphinsight.com/
>
> GraphInsight is a C/C++ flexible and scalable application for 2D and 3D
> interactive graph exploration that we have developed at the University
> of Trento, Italy.
>
> GraphInsight is free to use for research purposes.
>
>
> Best Regards,
> Michele Dallachiesa
>
> On Jun 7, 2012, at 12:10 AM, yyahn wrote:
>
>> *****  To join INSNA, visit http://www.insna.org  *****
>>
>> A couple of suggestions:
>>
>> - You can compare the measured clustering coefficient with null 
>> models (e.g. random graph model with the same degree sequence 
>> http://www.pnas.org/content/99/suppl.1/2566.full ). You can also plot 
>> and see local clustering coefficient as a function of degree.
>>
>> - Assortativity: http://en.wikipedia.org/wiki/Assortativity
>>
>> - One way to visualize huge graphs is getting coarse-grained 
>> representations such as communities and drawing the relationship 
>> between them (the network of communities. For example, check out Fig. 
>> 3 from http://arxiv.org/pdf/0803.0476v2.pdf)
>>
>> - There are many community identification methods that scale quite 
>> well. For instance Louvain method is known to scale very well. Our 
>> link community method can also be applied to networks with 
>> multi-million nodes if the network doesn't have very large hubs.
>>
>> Louvain method: https://sites.google.com/site/findcommunities/
>> Link community paper: 
>> http://www.nature.com/nature/journal/v466/n7307/abs/nature09182.html
>> C++ version of link clustering: 
>> https://github.com/bagrow/linkcomm/tree/master/cpp
>> A review on community detection: http://arxiv.org/abs/0906.0612
>>
>>
>> Best,
>> yy
>>
>> -- 
>> Yong-Yeol Ahn
>> Assistant Professor
>> School of Informatics and Computing
>> Indiana University Bloomington
>> Web: http://yongyeol.com
>>
>> On Jun 6, 2012, at 5:17 PM, Hyokun Yun wrote:
>>
>>> ***** To join INSNA, visit http://www.insna.org ***** Dear list 
>>> members,
>>>
>>>
>>> I would like to gather suggestions on:
>>>
>>> When confronted by a large network data (1M to 10M nodes, 100M to 1b 
>>> edges),
>>> what are your favorite first steps to understand the data and to 
>>> figure out the data makes sense?
>>>
>>>
>>> I agree that it may depend on what is the objective of the analysis, 
>>> but I think there should be
>>> certain steps those might be very common in many projects 
>>> irrespective of what the goal is.
>>>
>>>
>>>
>>> Here are a list of things that comes into my mind:
>>>
>>> 1) draw (in/out) degree distribution and check whether it is 
>>> long-tailed / follows power-law
>>> 2) hop plot (distribution of the number of pairs as a function of 
>>> geodesic distance), approximated by ANF method
>>> 3) number of (weak/strong) components and their sizes
>>> 4) apply scalable clustering algorithms (ex: METIS/Graclus) and get 
>>> profile information of each cluster
>>>
>>>
>>> Followings are some other methods but I am not sure how to apply:
>>>
>>> 1) calculate approximate clustering coefficient - but what should I 
>>> do with this number? Compare with clustering coefficients of 
>>> previously known networks?
>>> 2) visualize the graph - but I am not sure whether there is an 
>>> algorithm which will scale to 1M nodes, and even if there is, how 
>>> would I make sense out of it.
>>> 3) apply community detection algorithms - but is there an algorithm 
>>> that would scale to 1M node graphs? How would it be different from 
>>> graph clustering algorithms such as Graclus?
>>>
>>>
>>> Suggestion in any form including pointer to papers/books should be 
>>> very appreciated!
>>>
>>>
>>>
>>> Thanks,
>>> Hyokun Yun
>>>
>>> Ph.D Student
>>> Department of Statistics
>>> Purdue University
>>> _____________________________________________________________________ SOCNET 
>>> is a service of INSNA, the professional association for social 
>>> network researchers (http://www.insna.org). To unsubscribe, send an 
>>> email message to [log in to unmask] containing the line 
>>> UNSUBSCRIBE SOCNET in the body of the message.
>>
>> _____________________________________________________________________
>> SOCNET is a service of INSNA, the professional association for social
>> network researchers (http://www.insna.org). To unsubscribe, send
>> an email message to [log in to unmask] containing the line
>> UNSUBSCRIBE SOCNET in the body of the message.
>
> -- 
> ----------------------------------------------------------------------
> Michele Dallachiesa
> PhD candidate
>
> Database and Information Management Group
> DISI - University of Trento
> Via Sommarive, 14 - 38123 Povo (TN) - Italy
> ......................................................................
> Academic homepage: http://disi.unitn.it/~dallachiesa
> Interactive graph exploration: http://www.graphinsight.com
> ----------------------------------------------------------------------
>
> _____________________________________________________________________
> SOCNET is a service of INSNA, the professional association for social
> network researchers (http://www.insna.org). To unsubscribe, send
> an email message to [log in to unmask] containing the line
> UNSUBSCRIBE SOCNET in the body of the message.

-- 
Guy Melançon
CNRS UMR 5800 LaBRI
INRIA Bordeaux -- Sud-Ouest
Campus Université Bordeaux I
351 Cours de la libération
33405 Talence Cedex
France

Tel. +33 540 008 881
Fax +33 540 006 669
Bureau 308

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.