MLG-2010: Call for Participation
Call for Participation
Eighth Workshop on Mining and Learning with Graphs 2010 (MLG-2010)
http://www.cs.umd.edu/mlg2010
Washington, DC, July, 24-25
(co-located with KDD 2010
This year’s workshop on Mining and Learning with Graphs will be held in
conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery
and Data Mining that will take place in July 25-28, 2010 in Washington,
DC.
The importance of being able to effectively mine and learn from data
that is best represented as a graph is growing, as more and more
structured and semi-structured data is becoming available. Examples
include the WWW, social networks, biological networks, communication
networks, food webs, and many others. Traditionally, a number of
subareas have worked with mining and learning from graph structured
data, including communities in graph mining, learning from structured
data, statistical relational learning, inductive logic programming, and,
moving beyond sub-disciplines in computer science, social network
analysis, and, more broadly network science. The objective of this
workshop is to bring together researchers from a variety of these areas,
and discuss commonality and differences in challenges faced, survey some
of the different approaches, and provide a forum to present and learn
about some of the most cutting edge research in this area. As an
outcome, we expect participants to walk away with a better sense of the
variety of different tools available for graph mining and learning, and
an appreciation for some of the interesting emerging applications for
mining and learning from graphs.
Registration
You can register via http://www.sigkdd.org/kdd2010/registration.shtml
Program
Each day will consist of keynote speakers, short presentations
showcasing accepted papers, discussions at end of sessions, and a poster
session to promote dialog. A tentative schedule is available online
http://www.cs.umd.edu/mlg2010/schedule.html
featuring the following invited talks and accepted papers:
Invited Talks
* Stephen Fienberg, CMU
* Thomas Gärtner, University of Bonn and Fraunhofer IAIS
* Aristides Gionis, Yahoo! Research
* Jennifer Neville, Purdue University
* Padhraic Smyth, UCI
* Chris Volinsky, AT&T Labs
* Eric Xing, CMU
Accepted Papers
* Time-Based Sampling of Social Network Activity Graphs
Nesreen Ahmed, Fredrick Berchmans, Jennifer Neville and Ramana
Kompella
* Structure, Tie Persistence and Event Detection in Large Phone
and SMS Networks
Leman Akoglu and Bhavana Dalvi
* SVM Optimization for Lattice Kernels
Cyril Allauzen, Corinna Cortes and Mehryar Mohri
* A Compact Representation of Graph Databases
Sandra Álvarez, Nieves R. Brisaboa, Susana Ladra and Óscar
Pedreira
* Binary Bit String Representation for Networks based on
Exchangeable Graph Modeling
Hossein Azari, Edoardo Airoldi and Vahid Tarokh
* A Community-Based Model of Online Social Networks
Leendert Botha and Steve Kroon
* Enhancing Link-Based Similarity Through the Use of Non-Numerical
Labels and Prior Information
Christian Desrosiers and George Karypis
* Network Community Discovery: Solving Modularity Clustering via
Normalized Cut
Chris Ding and Linbin Yu
* Analyzing Graph Databases by Aggregate Queries
Anton Dries and Siegfried Nijssen
* Multi-Network Fusion for Collective Inference
Hoda Eldardiry and Jennifer Neville
* Bayesian Block Modelling for Weighted Networks
Ian Gallagher
* An efficient block model for clustering sparse graphs
Adam Gyenge, Janne Sinkkonen and Andras A. Benczur
* Centrality Metric for Dynamic Networks
Kristina Lerman, Rumi Ghosh and Jeon Hyung Kang
* Design Patterns for Efficient Graph Algorithms in MapReduce
Jimmy Lin and Michael Schatz
* Prioritizing Candidate Genes by Network Analysis of Differential
Expression using Machine Learning Approaches
Daniela Nitsch
* Document Classification Utilising Ontologies and Relations
between Documents
Katariina Nyberg, Tapani Raiko, Teemu Tiinanen and Eero Hyvönen
* Graph Visualization With Latent Variable Models
Juuso Parkkinen, Kristian Nybo, Jaakko Peltonen and Samuel Kaski
* Relational motif discovery via graph spectral ranking
Alberto Pinto
* Symmetrizations for Clustering Directed Graphs
Venu Satuluri and Srinivasan Parthasarathy
* Pruthak- mining and analyzing graph substructures
Swapnil Shrivastava, Kriti Kulshrestha, Pratibha Singh and
Supriya N Pal
* Structural Correlation Pattern Mining for Large Graphs
Arlei Silva, Wagner Meira Jr. and Mohammed J. Zaki
* Meaningful Selection of Temporal Resolution for Dynamic Networks
Rajmonda Sulo, Tanya Berger-Wolf and Robert Grossman
* Community Evolution Detection in Dynamic Heterogeneous
Information Networks
Yizhou Sun, Jie Tang, Jiawei Han, Manish Gupta and Bo Zhao
* Network Quantification Despite Biased Labels
Lei Tang, Huiji Gao and Huan Liu
* Frequent Subgraph Discovery in Dynamic Networks
Bianca Wackersreuther, Peter Wackersreuther, Annahita Oswald,
Christian Böhm and Karsten Borgwardt
* Querying Graphs with Uncertain Predicates
Hao Zhou, Anna Shaverdian, H. V. Jagadish and George Michailidis
* Frequent Subgraph Mining on a Single Large Graph Using Sampling
Techniques
Ruoyu Zou and Lawrence Holder
We look forward to seeing you in Washington!
Lise Getoor, Sofus Macskassy, and Ulf Brefeld
