Ninth Workshop on Mining and Learning with Graphs (MLG-2011) @KDD 2011

Call for Participation

Ninth Workshop on Mining and Learning with Graphs 2011 (MLG-2011)
San Diego, CA, USA, August, 20-21, 2011
(co-located with KDD 2011)

*** Register before July 31st for discounted rates ***

This year's workshop on Mining and Learning with Graphs will be held in
conjunction with the 17th ACM SIGKDD Conference on Knowledge Discovery
and Data Mining that will take place in August 21-24, 2011, in San Deigo,

There is a growing need and interest in analyzing data that is best
represented as a graph, such as the World Wide Web, social networks, social
media, biological networks, communication networks, and physical network
systems.  Traditionally, methods for mining and learning with such graphs
has been studied independently in several research areas, including machine
learning, statistics, data mining, information retrieval, natural language
processing, computational biology, statistical physics, and sociology.
However, we note that contributions developed in one area can, and should,
impact work in the other areas and disciplines. One goal of this workshop is
to foster this type of interdisciplinary exchange, by encouraging
abstraction of the underlying problem (and solution) characteristics.

To reflect the broad scope of work on mining and learning with graphs,
we encouraged submissions that span the spectrum from theoretical analysis
methods, to algorithms and implementation, to applications and empirical
studies. In terms of application areas, the growth of user-generated
content on blogs, microblogs, discussion forums, product reviews, etc.,
has given rise to a host of new opportunities for graph mining in the
analysis of Social Media. Social Media Analytics is a fertile ground for
research at the intersection of mining graphs and text. As such, this
year we especially encouraged submissions on theory, methods, and
focusing on the analysis of social media.


You can register via


Both days will consist of keynote speakers and short presentations
showcasing accepted papers. To promote dialog we will have a
panel discussion and a poster session.

The program features the following invited talks and accepted papers:

Invited Talks

* Lada Adamic, University of Michigan
* Karsten Borgwardt, Max Planck Institute
* William Cohen, Carnegie Melon University
* Charles Elkan, University of California, San Diego
* Michelle Girvan, University of Maryland
* Alon Halevy, Google Inc.
* Peter Hoff, Univeristy of Washington
* Michael Mahoney, Stanford University

Accepted Papers

* Comparing Generalizations of Unweighted Network Measures
Sherief Abdallah and Habab Musa

* A Framework for Hypothesis Learning Over Sets of Vectors
Karim Abou-Moustafa and Frank Ferrie

* All-at-once Optimization for Coupled Matrix and Tensor Factorizations
Evrim Acar, Tamara Kolda and Daniel Dunlavy

* Evaluating Markov Logic Networks for Collective Classification
Robert Crane and Luke Mcdowell

* Graph Based Statistical Analysis of Network Traffic
Hristo Djidjev, Gary Sandine, Curtis Storlie and Scott Vander Wiel

* Compression versus Frequency for Mining Patterns and Anomalies in Graphs
William Eberle and Lawrence Holder

* Learning from Plane Graphs
Thomas Fannes and Jan Ramon

* Bayesian Optimal Active Search on Graphs
Roman Garnett, Yamuna Krishnamurthy, Donghan Wang, Jeff Schneider and
Richard Mann

* Can’t See Forest through the Trees? Understanding Mixed Network Traffic
Graphs from Application Class Distribution
Yu Jin, Nick Duffield, Patrick Haffner, Subhabrata Sen and Zhi-Li Zhang

* Graph Classification via Topological and Label Attributes
Geng Li, Murat Semerci, Bülent Yener and Mohammed Zaki

* Adaptation of Graph-Based Semi-Supervised Methods to Large-Scale Text Data
Frank Lin and William W. Cohen

* Building a Semantic Graph based on Sequential Language Model for
Content Extraction
Yan Liang and Ying Liu

* Small Network Segmentation with Template Guidance
Kristin Lui and Ian Davidson

* Generating Similar Graphs from Spherical Features
Dalton Lunga and Sergey Kirshner

* Predicting Dynamic Difficulty
Olana Missura and Thomas Gaertner

* Mining Differential Hubs in Homogenous Networks
Omar Odibat and Chandan K. Reddy

* Integrating Logic Knowledge into Graph Regularization: an application to
image tagging
Claudio Sacca, Michelangelo Diligenti, Marco Gori and Marco Maggini

* Exploring Co-Occurrence on a Meso and Global Level Using Network Analysis
and Rule Mining
Maximilian Schich and Michele Coscia

* Transductive Classification Methods for Mixed Graphs
Sundararajan Sellamanickam and Sathiya Keerthi

* Graph Mining of Motif Profiles For Computer Network Activity Inference
William Turkett, Errin Fulp, Charles Lever and Edward Allan

* Trading off Propagation and Misspecification Error to Reduce Inference
in Probabilistic Relational Models
Rongjing Xiang and Jennifer Neville

* Visualizing the Temporal Evolution of Dynamic Networks
Kevin Xu, Mark Kliger and Alfred Hero

We look forward to seeing you in San Diego!

Kristian Kersting, Fraunhofer IAIS and University of Bonn
Prem Melville, IBM Research
Jennifer Neville, Purdue University
C. David Page Jr., University of Wisconsin Medical School

Received on Sunday, 17 July 2011 18:46:06 UTC