Last day for enjoying early bird discount for our 3rd GraphLab Conference!

• 
  Schedule
• Sponsors
• Venue
• For presenters
• Program Committee

We have just started to organize our 3rd user conference on Monday July 21, 2014 at the Nikko Hotel, SF. This is a very preliminary notice to attract companies and universities who like to be involved. We are planning a mega event this year with around 800-900 data scientists attending, with the topic of graph analytics and large scale machine learning.

The conference is a non-profit event held by GraphLab.org to promote applications of large scale graph analytics in industry. We invite talks from all major state-of-the-art systems for graph processing, graph databases and large scale data analytics and machine learning. We are looking for sponsors who would like to contribute to the event organization.

Preliminary talks:

• Reynold Xin, co-Founder of Databricks will present Machine Learning and Graph Computation on Spark
• Wes McKinney, Founder & CEO of DataPad – TBA
• Prof. Carlos Guestrin, Founder & CEO of GraphLab will present GraphLab
• Prof. Vahab Mirrokni from Google Google Research New York team – ASYMP: Fault-tolerant Graph Mining via ASYnchronous Message Passing
• Prof. Joe Hellerstein, Founder & CEO of Trifacta – TBA
• Tao Ye, Senior Scientist, Pandora Internet Radio – TBA
• Josh Wills, Director of Data Science at Cloudera – TBA
• Milind Bhandarkar, Chief Scientist at Pivotal – The Zoo Expands: Labrador 💛 Elephant thanks to Hamster
• Dr. Markus Weimer,Microsot Research - REEF: Towards a Big Data stdlib
• Karthik Ramachandran and Erick Tryzelaar, Lab41: Dendrite large scale graph analytics

Preliminary demos:

	Dr. Ari Tuchman: Beyond Sentiment and Buzz: Extracting the Answers that Matter Though Predictive Correlations from Unstructured Chatter
	TBA
	TBA
	Paul Hoffman: Large Scale Machine Learning on Sparse Graphs
	Dr. Jans Aasman, CEO, Franz Inc. Drag and Drop Graph Query Generator
	Dr. Zhisong Fu, Mike Personick, and Bryan Thompson: Ultra fast graph mining on GPUs.
	TBA
	TBA
	Tristan Zajonc and Anand Patil: Sense
	Dr. David Talby: Beyond ML basics: Localized, evolving, hybrid & automated modeling at scale
	Simon Chan: An Open Source Machine Learning Server for Developers
	TBA
	Adam Fuchs, CTO Sqrrl: How To Build Secure, Massively Scalable Graphs with Sqrrl
	Dr. Steven Hillion, Alpine Data Labs: Fast classification algorithms on Hadoop
	Jacob Nelson: Grappa graph engine
	Prof. Joshua Bloom, wiso.io: Machine-learning Driven Automated Insight Workflows
	Dr. Matthias Broecheler, Titan – Scalable Graph Computing in Real-time and Offline
	TBA
	Prof. Eric Xing: Petuum – a new distributed machine learning framework
	Corey Lanum, General Manager of North America, Cambridge Intelligence: How to make useful interactive graph visualizations
	Dr. Ira Cohen, HP Software: Scaling the data scientist
	Brendan Madden, Tom Sawyer Software: TBA
	Dr. Jason Riedy, Georgia Tech: STING: High-Performance Analysis for Streaming Graph Data
	Dr. Hassan Chafi, Oracle: Graph Analytics Research at Oracle Labs
	Dr. Achim Rettinger, EPPICS: Cross-lingual Cross-modal Analytics of Dynamic Graphs
	TBA
	Corinna Bahr, Continuum.io: Agile Data Exploration & Visualization with Blaze and Bokeh
Graphistry	Leo Meyerovich, Graphistry: Scaling Visualization with Design and GPUs
Domino Data Labs	Nick Elprin: Domino Data Labs
	Dr. Fernando Perez, Berkeley: IPython: from interactive computing to computational narratives
	Dr. Linas Baltrunas and Dr. Dionysos Logothetis:, Telefonica Research:Grafos.ml: Tools for large scale ML and graph analysis
	Ms. Raquel Pau, Sparsity Technologies: Tweeticer, Social Network Analysis with graphs using Sparksee.
	SriSatish Ambati, co-founder and CEO: TBA
	Jonathan Dinu, CTO Zipfian Academy: TBA
	Demian Bellumio, COO Senzari: MusicGraph
	Sutanay Choudhury, Pacific Northwest National Lab: M&Ms4Graphs: Multi-scale, Multi-dimensional Graph Analytics Tools for Cyber-Security
	Michael Zeller, CEO Zementis: Accelerate predictive analytics with massively parallel scoring
	Sébastien Heymann CEO and Jean Villedieu Co-founder, Linkurious: How can graph visualization help understand graphs faster?
	Richard Socher, Stanford: etcML project
	MongoDB: TBA
	Amit Moran, Crosswise: TBA

Graphs are everywhere - and now food graph!

There isn't a single day where I hear about a new system, or an academic project who is utilizing graphs for getting additional insights out of the data. Today I heard about an interesting study of taste from Prof. Alon Ben-Ari, Director Medical Informatics Fellowship Program, University of Washington - VA Medical Center:

Ahn, Yong-Yeol, Sebastian E. Ahnert, James P. Bagrow, and Albert-László Barabási. "Flavor network and the principles of food pairing." Scientific reports 1 (2011). 

This paper analyses connections between recipe components using graphs. For each pair of ingredients that appear in a recipe together a graph edge is created.

Pivotal backs up GraphLab as part of its HD offering

Fresh news just announced:

Pivotal HD 2.0 expands analytic use cases with integration and support of GraphLab, MADlib, and popular languages and formats such as R, Python, Java, and Parquet to create a powerful and easy to use analytical platform for data scientists and analysts in Hadoop.

...
Also new within Pivotal HD is the world's first enterprise integration of GraphLab, an advanced set of algorithms for graph analytics that enables data scientists and analysts to leverage popular algorithms for insight, i.e. page rank, collaborative filtering and computer vision.

Anyone who wants to learn more about Pivotal and GraphLab integration should attend our 3rd GraphLab conference where Milind Bhandarkar, Chief Scientist at Pivotal – will give a talk titled: "The Zoo Expands:  Labrador ♥ Elephant thanks to Hamster"

Spotlight: SiSense

Ben Lorica our man in O'Reilly Media sent me a link to this interesting Israeli company. It seems they are doing in memory and out of core computation on a single multicore machine to scale to large datasets, product some statistics which are turned into web reports. Here is their demo video:

According to their website they have some customers like ebay and NASA.

A very impressive performance is demonstrated here: 10TB of parsed data on 10 seconds on a 10,000$ server.

Related blog posts: HP Software's Titan system , Alpine Data Labs.

Spotlight: 0xdata

Just learned about 0xdata open source project (pronounced hex-data). It has a library called H2O for predictive analytics that can work either standalone or on top of Hadoop map reduce. H2O has interfaces to Scala and Java, and also R.

So far  H2O supports generalized linear models, decision trees and K-means clustering. According to their website they have Netflix and Trulia as customers.

Anyone who is interested in learning more about 0xdata is welcome to attend our 3rd GraphLab Conference where 0xdata will give a demo of their H2O library.

Spotlight: Ravel Law - introducing graph analytics to law research

Pranav Singh reached out to me, as he is a data scientist working with Ravel Law for analyzing law related datasets. It seems like an interesting vertical of applying big data analytics to court decisions.

Recently Ravel Law started to incorporate graph data into their analysis. While some of their research is proprietary, they where kindly willing to share some published results. Pranav sent me a paper by Fowler which is named "Network Analysis and the Law".  It shows that using basic pagerank algorithm (hub / authorities) you can get very deep insights into supreme court decision and their importance. The algorithm is rather basic but the applications for the Law vertical are rather new, at least to me.

University of Waterloo evaluates GraphLab vs. other BSP systems

Just got a link to a blog post which details experiments done at the University of Waterloo by a master student Prashant Raghav. The experiments compare Giraph, GPS, GraphLab and GraphChi, comparing both memory footprint and runtime. If you like to know which system performs better you should read the bog post.