Archive for the ‘Graphity’ Category

WeST researcher’s summary of SocialCom 2012 in Amsterdam

Friday, September 21st, 2012

WeST researcher’s summary of SocialCom 2012 in Amsterdam by René Pichardt.

René has a new principal blogging site and reports on SocialCom 2012.

In the beginning of this month I was attending my first major conferenc IEEE SocialComp2012 in Amsterdam. I was presenting my work Graphity. On the following URL you can find the slides, videos, data sets, source code and of course the full paper!

www.rene-pickhardt.de/graphity

In this article I want to talk about the conference itself. About what presentations I particularly liked and share some impressions.

I got distracted by the Graphity paper but promise I will read the rest of René’s comments on the conference this weekend!

Typology Oberseminar talk and Speed up of retrieval by a factor of 1000

Thursday, August 16th, 2012

Typology Oberseminar talk and Speed up of retrieval by a factor of 1000
by René Pickhardt.

From the post:

Almost 2 months ago I talked in our oberseminar about Typology. Most readers of my blog will already know the project which was initially implemented by my students Till and Paul. I am just about to share some slides with you. They explain on one hand how the systems works and on the other hand give some overview of the related work.
As you can see from the slides we are planning to submit our results to SIGIR conference. So one year after my first blogpost on graphity which devoloped in a full paper for socialcom2012 (graphity blog post and blog post for source code) there is the yet informal typology blog post with the slides about the Typology Oberseminar talk and 3 months left for our SIGIR submission. I expect this time the submission will not be such a hassle as graphity since I shuold have learnt some lessons and also have a good student who is helping me with the implementation of all the tests.

Time remains for you to make suggestions!

Graphity source code and wikipedia raw data

Monday, July 9th, 2012

Graphity source code and wikipedia raw data is online (neo4j based social news stream framework) René Pickhardt.

From the post:

8 months ago I posted the results of my research about fast retrieval of social news feeds and in particular my graph index graphity. The index is able to serve more than 12 thousand personalized social news streams per second in social networks with several million active users. I was able to show that the system is independent of the node degree or network size. Therefor it scales to graphs of arbitrary size.

Today I am pleased to anounce that our joint work was accepted as a full research paper at IEEE SocialCom conference 2012. The conference will take place in early September 2012 in Amsterdam. As promised before I will now open the source code of Graphity to the community. Its documentation could / and might be improved in future also I am sure that one is even able to use a better data structure for our implementation of the priority queue.

Still the attention from the developer community for Graphity was quite high so maybe the source code is of help to anyone. The source code consists of the entire evaluation framework that we used for our evaluation against other baselines which will also help anyone to reproduce our evaluation.

There is some nice things one can learn in setting up multthreading for time measurements and also how to set up a good logging mechanism.

Just in case you are interested in all the changes ever made to the German entries in Wikipedia.

That’s one use case. 😉

Deeply awesome work!

Please take a close look! This looks important!

Graphity: An efficient Graph Model for Retrieving the Top-k News Feeds for users in social networks

Sunday, November 20th, 2011

Graphity: An efficient Graph Model for Retrieving the Top-k News Feeds for users in social networks by Rene Pickhardt.

From the post:

I already said that my first research results have been submitted to SIGMOD conference to the social networks and graph databases track. Time to sum up the results and blog about them.

I created a data model to make retrieval of social news feeds in social networks very efficient. It is able to dynamically retrieve more than 10’000 temporal ordered news feeds per second in social networks with millions of users like Facebook and Twitter by using graph data bases (like neo4j)

10,000 temporally ordered news feeds per second? I can imagine any number of use cases that fit comfortably within those performance numbers!

How about you?

Looking forward to the paper (and source code)!