5. MLG 2007: Firenze, Italy

Session 1

• Pierre Baldi:
  Learning and Charting Chemical Space with Strings and Graphs: Challenges and Opportunities for AI and Machine Learning.
  
• Mario Boley, Tamás Horváth, Axel Poigné, Stefan Wrobel:
  Efficient Closed Pattern Mining in Strongly Accessible Set Systems.
  
• Ulf Brefeld, Thoralf Klein, Tobias Scheffer:
  Support Vector Machines for Collective Inference.
  
• Thomas Gärtner, Gemma C. Garriga:
  The Cost of Learning Directed Cuts.
  
• Kaspar Riesen, Stefan Fankhauser, Horst Bunke:
  Speeding Up Graph Edit Distance Computation with a Bipartite Heuristic.
  

Session 2

• Luc De Raedt:
  ProbLog and its Application to Link Mining in Biological Networks.
  
• Manfred Jaeger, Petr Lidman, Juan L. Mateo:
  Comparative Evaluation of PL languages.
  
• Kristiaan Pelckmans, Johan A. K. Suykens:
  Transductive Rademacher Complexities for Learning Over a Graph.
  
• Hendrik Blockeel, Tijn Witsenburg, Joost N. Kok:
  Graphs, Hypergraphs, and Inductive Logic Programming.
  
• Yana Volkovich, Nelly Litvak, Debora Donato:
  Web Graph Parameters and the Pagerank Distribution.
  
• Shankar Vembu, Thomas Gärtner, Stefan Wrobel:
  Semidefinite Ranking on Graphs.
  
• Davide Bacciu, Alessio Botta, Dan Stefanescu:
  Augmenting the Distributed Evaluation of Path Queries on Data-Graphs with Information Granules.
  

Session 3

• Jiawei Han:
  Mining, Indexing, and Searching Graphs in Large Data Sets.
  
• Mathias Fiedler, Christian Borgelt:
  Support Computation for Mining Frequent Subgraphs in a Single Graph.
  
• Jan Ramon, Siegfried Nijssen:
  General Graph Refinement with Polynomial Delay.
  
• Christian Desrosiers, Philippe Galinier, Pierre Hansen, Alain Hertz:
  Improving Frequent Subgraph Mining in the Presence of Symmetry.
  
• Alexandre Termier, Yoshinori Tamada, Kazuyuki Numata, Seiya Imoto, Takashi Washio, Tomoyuki Higuchi:
  DIGDAG, a First Algorithm to Mine Closed Frequent Embedded Sub-DAGs.
  

Session 4

• Lise Getoor:
  Graph Identification.
  
• Shawndra Hill, Foster J. Provost, Chris Volinsky:
  Learning and Inference in Massive Social Networks.
  
• Jianzhong Chen, Stephen Muggleton, Jose Santos:
  Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning.
  
• Karsten M. Borgwardt, Tobias Petri, S. V. N. Vishwanathan, Hans-Peter Kriegel:
  An Efficient Sampling Scheme For Comparison of Large Graphs.
  
• Zhao Xu, Volker Tresp, Shipeng Yu, Kai Yu, Hans-Peter Kriegel:
  Fast Inference in Infinite Hidden Relational Models.
  

Session 5

• Alex J. Smola:
  Learning Graph Matching.
  
• Janne Sinkkonen, Janne Aukia, Samuel Kaski:
  Inferring Vertex Properties from Topology in Large Networks.
  
• Leander Schietgat, Jan Ramon, Maurice Bruynooghe:
  A Polynomial-time Metric for Outerplanar Graphs.
  
• Sebastian Nowozin, Koji Tsuda, Takeaki Uno, Taku Kudo, Gökhan H. Bakir:
  Weighted Substructure Mining for Image Analysis.
  
• Thomas Gärtner:
  Efficient Kernel Methods for Graphs.
  
• Leonid Kontorovich:
  A Universal Kernel for Learning Regular Languages.