# List of accepted papers

## Talks

ID | Title | Primary Contact Author |
---|---|---|

2 | A New Kernel for Classification of Networked Entities | Zhang, Dell |

6 | Efficient Discriminative Training Method for Structured Predictions | Yu, Huizhen |

7 | Representative Subgraph Sampling using Markov Chain Monte Carlo Methods | Borgwardt, Karsten |

9 | Combining near-optimal feature selection with gSpan | Borgwardt, Karsten |

10 | Infinite mixtures for multi-relational categorical data | Sinkkonen, Janne |

11 | Parameter Learning in Probabilistic Databases: A Least Squares Approach | Gutmann, Bernd |

13 | Structure and tie strengths in a mobile communication network | Saramäki, Jari |

18 | Induction of Node Label Controlled Graph Grammar Rules | Blockeel, Hendrik |

19 | Classification in Graphs using Discriminative Random Walks | Françoisse, Kevin |

20 | Min, Max and PTIME Anti-Monotonic Overlap Graph Measures | Van Dyck, Dries |

21 | Improved Software Fault Detection with Graph Mining | Eichinger, Frank |

23 | Markov Logic Improves Protein β-Partners Prediction | Frasconi, Paolo |

28 | Comparing Diffusion Models for Graph--Based Semi--Supervised Learning | Galstyan, Aram |

31 | A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery | Blaschko, Matthew |

43 | Inferring the structure and scale of modular networks | Hofman, Jake |

44 | An Online Algorithm for Learning a Labeling of a Graph | Pelckmans , Kristiaan |

## Posters

ID | Title | Primary Contact Author |
---|---|---|

3 | Mining Common Semantic Patterns from Descriptions of Failure Knowledge | Kraines, Steven |

4 | A graph-theoretic approach for reducing one-versus-one multi-class classification to ranking | Waegeman, Willem |

5 | The skew spectrum of graphs | Kondor, Risi |

8 | Probabilistic models for the dynamics of tree-structured data | Dalvi, Nilesh |

12 | Randomization Techniques for Statistical Significance Testing on Graphs | Hanhijärvi, Sami |

15 | The All-Paths Covariance: a new covariance measure between nodes of a weighted, directed, graph | Mantrach, Amin |

17 | Scalable Algorithms for Structured Output Prediction | Vembu, Shankar |

24 | Mining music graphs through immanantal polynomials | Pinto, Alberto |

27 | CPT-L: an Efficient Model for Relational Stochastic Processes | Thon, Ingo |

32 | Mining graphs to discover new theorems in mathematics | Desrosiers, Christian |

33 | Graph Theoretic Measures for Identifying Affective Blockers of Spreading Processes in Dynamic Networks | Habiba, Habiba |

34 | A Structured-Outputs Method for Prediction of Protein Function | Sokolov, Artem |

37 | Prediction of Molecular Substructures from Mass Spectrograms Using Constraint Based Clustering | Drouillon, Pieter-Jan |

38 | A Method to extend Existing Document Clustering Procedures in order to include Relational Information | Witsenburg, Tijn |

39 | High-Order Regularization on Graphs | Zhou, Dengyong |

41 | A structured outputs method for predicting protein binding sites | Hamilton, Michael |

42 | Combining Optimal and Atomic Decomposition of Terminology Association Graphs | Meurs, Marie-Jean |