o Overview

o Schedule

o Invited Speakers

o Papers

o Organizers

o Sponsor

The 11th Workshop on Mining and Learning with Graphs (MLG'13)

Introduction

There is a great deal of interest in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, transportation networks, energy grids, and many others. These graphs are typically multi-modal, multi-relational and dynamic. In the era of big data, the importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available. The workshop serves as a forum for researchers from a variety of fields working on mining and learning from graphs to share and discuss their latest findings.

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Schedule Return to Top

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Tentative Schedule
August 11, 2013 Sunday
09:00-9:35	Evimaria Terzi
9:35-10:00	Spotlights A
10:00-10:30	Coffee break
10:30-11:05	Sam Shah
11:05-11:40	David Bader
11:40-12:00	Spotlights B
12:00-2:00	Lunch break
2:00-2:35	Tina Eliassi-Rad
2:35-3:10	Evgeniy Gabrilovich
3:10-4:25	Coffee break + poster session
4:25-5:00	David Gleich
5:00	Wrap up

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Invited Speakers Return to Top

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David Bader, Georgia Institute of Technology
Tina Eliassi-Rad, Rutgers University
Evgeniy Gabrilovich, Google Research
David Gleich, Purdue University
Sam Shah, LinkedIn
Evimaria Terzi, Boston University

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Contributed Papers Return to Top

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• Auto-correlation Dependent Bounds for Relational Data
  Amit Dhurandhar.
  
  
• Application of Group Testing in Identifying High Betweenness Centrality Vertices in Complex Networks
  Vladimir Ufimtsev and Sanjukta Bhowmick.
  
  
• Extended Tensor Factorization for learning new facts in Knowledge Bases
  Tanmoy Mukherjee and Vinay Pande.
  
  
• The Weibull as a Model of Shortest Path Distributions in Random Networks
  Christian Bauckhage, Kristian Kersting and Bashir Rastegarpanah.
  
  
• p-voltages: Laplacian Regularization for Semi-Supervised Learning on High-Dimensional Data
  Nick Bridle and Xiaojin Zhu.
  
  
• Investors Are Social Animals: Predicting Investor Behavior using Social Network Features via Supervised Learning Approach
  Yuxian Eugene Liang and Yuan Soe-Tsyr Daphne.
  
  
• Polynomial-Time Algorithm for Finding Densest Subgraphs in Uncertain Graphs
  Zhaonian Zou.
  
  
• Network Traffic Analysis Using Principal Component Graphs
  Harshasai Thota, Vijaya Saradhi V and Venkatesh T.
  
  
• Directional Component Detection via Markov Clustering in Directed Networks
  Yu-Keng Shih, Sungmin Kim, Tao Shi and Srinivasan Parthasarathy.
  
  
• Graph Kernels for Object Category Prediction in Task-Dependent Robot Grasping
  Marion Neumann, Plinio Moreno, Laura Antanas, Roman Garnett and Kristian Kersting.
  
  
• Block Kronecker Product Graph Model
  Sebastian Moreno, Pablo Robles and Jennifer Neville.
  
  
• Degrees of separation on a dynamic social network
  Andre Domingos, Hugo Ferreira, Pedro Rijo, Catia Vaz and Alexandre P. Francisco.
  
  
• Spanning edge betweenness
  Andreia Sofia Teixeira, Pedro T. Monteiro, Joao Carrico, Mario Ramirez and Alexandre P. Francisco.
  
  
• Towards mining and learning with networked examples
  Yuyi Wang and Jan Ramon.
  
  
• Coinciding Walk Graph Kernels
  Roman Garnett, Marion Neumann and Kristian Kersting.
  
  
• Social Network Dynamics in a Massive Online Game: Network Turnover, Non-densification, and Team Engagement in Halo Reach
  Sears Merritt and Aaron Clauset.
  
  
• Analyzing Social Media Relationships in Context with Discussion Graphs
  Emre Kiciman, Munmun De Choudhury, Scott Counts and Michael Gamon.
  
  
• Socially Relevant Venue Clustering from Check-in Data
  Yoon-Sik Cho, Greg Ver Steeg and Aram Galstyan.
  
  
• Will We Connect Again? Machine Learning for Link Prediction in Mobile Social Networks
  Ole Mengshoel, Raj Desai, Andrew Chen and Brian Tran.
  
  
• Multi-Mode Exponential Random Graph Models for Link Prediction in Biological Networks
  Ali Shojaie.
  
  
• Learning to Rank on Network Data
  Majid Yazdani, Ronan Collobert and Andrei Popescu-Belis.
  
  

Note: a few contributed papers are not included in these proceedings for copyright reasons. They are available on the official MLG website http://snap.stanford.edu/mlg2013/

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Organizers Return to Top

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Organizing Committee

• Lada Adamic (Facebook)
• Lise Getoor (University of Maryland)
• Bert Huang (University of Maryland)
• Jure Leskovec (Stanford University)
• Julian McAuley (Stanford University)

Program Committee

• Edoardo Airoldi, Harvard University
• Leman Akoglu, Stony Brook University
• Aris Anagnostopoulos, Sapienza University of Rome
• Arindam Banerjee, University of Minnesota
• Christian Bauckhage, Fraunhofer IAIS
• Francesco Bonchi, Yahoo! Research
• Ulf Brefeld, Technische Universität Darmstadt
• Tina Eliassi-Rad, Rutgers University
• Thomas Gaerner, Fraunhofer IAIS and University of Bonn
• Brian Gallagher, Lawrence Livermore National Laboratory
• David Gleich, Purdue University
• Marco Gori, University of Siena
• Mohammad Hasan, Indiana University-Purdue University Indianapolis
• Jake Hofman, Microsoft Research
• Jiawei Han, University of Illinois at Urbana-Champaign
• Larry Holder, Washington State University
• Manfred Jaeger, Aalborg University
• Tamara Kolda, Sandia National Laboratories
• U Kang, Carnegie Mellon University
• Kristian Kersting, Fraunhofer IAIS and University of Bonn
• Kristina Lerman, University of Southern California
• Bo Long, LinkedIn
• Sofus Macskassy, Facebook
• Thorsten Meinl, University of Konstanz
• Prem Melville, IBM Research
• Dunja Mladenic, J. Stefan Institute
• Jennifer Neville, Purdue University
• Srinivasan Parthasarathy, Ohio State University
• Jan Ramon, KU Leuven
• Bing Tian Dai, Singapore Management University
• Hanghang Tong, City University of New York
• Chris Volinsky, AT&T
• Stefan Wrobel, Fraunhofer IAIS and University of Bonn
• Xifeng Yan, University of California at Santa Barbara
• Xintian Yang, Ohio State University
• Philip Yu, University of Illinois at Chicago
• Mohammed Zaki, Rensselaer Polytechnic Institute
• Liang Zhang, LinkedIn
• Mark Zhang, Binghamton

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