Workshop on Mining and Learning with Graphs 2008 - Helsinki

6th International Workshop on Mining and Learning with Graphs (MLG), Helsinki 2008

Driven by application areas ranging from biology to the World Wide Web, research in Data Mining and Machine Learning is nowadays increasingly focusing on the analysis of structured data. Of particular interest is data that consists of interrelated parts or is characterized by collections of objects that are interrelated and linked together into complex graphs and structures. Following in the footsteps of the highly successful MLG workshops in the past, MLG 2008 again will be the premier forum for bringing together different sub-disciplines within Machine Learning and Data Mining that focus on the analysis of structured data. The workshop is actively seeking contributions dealing with all forms of structured data, including but not limited to graphs, trees, sequences, relations and networks.

Contributions are invited from all relevant disciplines, such as for example

Statistical Relational Learning
Inductive Logic Programming
Kernel Methods for Structured Data
Probabilistic Models for Structured Data
Graph Mining
(Multi-)relational Data Mining
Methods for Structured Outputs
Network Analysis


[syn] 2835 views, 06:04 Opening RemarksOpening Remarks Samuel Kaski Samuel Kaski
Session 1
[syn] 5652 views, 57:17 Invited speaker Four graph partitioning algorithmsFour graph partitioning algorithms Fan Chung Fan Chung	[syn] 3885 views, 27:31 Combining near-optimal feature selection with gSpanCombining near-optimal feature selection with gSpan Marisa Thoma Marisa Thoma	[syn] 4974 views, 26:45 Representative Subgraph Sampling using Markov Chain Monte Carlo MethodsRepresentative Subgraph Sampling using Markov Chain Monte Carlo Methods Karsten Michael Borgwardt Karsten Michael Borgwardt	[syn] 3478 views, 28:46 Inferring the structure and scale of modular networksInferring the structure and scale of modular networks Jake M. Hofman Jake M. Hofman	[syn] 3079 views, 18:47 Min, Max and PTIME Anti-Monotonic Overlap Graph MeasuresMin, Max and PTIME Anti-Monotonic Overlap Graph Measures Dries Van Dyck Dries Van Dyck
Session 2
[syn] 10469 views, 54:09 Invited speaker Influence and Correlation in Social NetworksInfluence and Correlation in Social Networks Mohammad Mahdian Mohammad Mahdian 4 comments	[syn] 4512 views, 31:01 Classification in Graphs using Discriminative Random WalksClassification in Graphs using Discriminative Random Walks Jerome Callut Jerome Callut	[syn] 3071 views, 26:48 An Online Algorithm for Learning a Labeling of a GraphAn Online Algorithm for Learning a Labeling of a Graph Kristiaan Pelckmans Kristiaan Pelckmans	[syn] 3054 views, 29:46 A New Kernel for Classification of Networked EntitiessA New Kernel for Classification of Networked Entitiess Dell Zhang Dell Zhang	[syn] 4370 views, 29:18 Induction of Node Label Controlled Graph Grammar RulesInduction of Node Label Controlled Graph Grammar Rules Hendrik Blockeel Hendrik Blockeel
[syn] 2658 views, 14:21 Poster SpotlightsPoster Spotlights Hannes Heikinheimo Hannes Heikinheimo
Session 3
[syn] 24279 views, 59:43 Invited speaker Structured Output Prediction with Structural SVMsStructured Output Prediction with Structural SVMs Thorsten Joachims Thorsten Joachims 1 comment	[syn] 3414 views, 37:41 Efficient Discriminative Training Method for Structured PredictionsEfficient Discriminative Training Method for Structured Predictions Huizhen Yu Huizhen Yu	[syn] 4165 views, 27:50 Structure and tie strengths in a mobile communication networkStructure and tie strengths in a mobile communication network Jari Saramaki Jari Saramaki	[syn] 4587 views, 32:18 Improved Software Fault Detection with Graph MiningImproved Software Fault Detection with Graph Mining Frank Eichinger Frank Eichinger
Session 4
[syn] 2932 views, 50:43 Invited speaker Biomine search engine for probabilistic graphsBiomine search engine for probabilistic graphs Hannu Toivonen Hannu Toivonen	[syn] 2943 views, 23:09 Parameter Learning in Probabilistic Databases: A Least Squares ApproachParameter Learning in Probabilistic Databases: A Least Squares Approach Bernd Gutmann Bernd Gutmann	[syn] 2989 views, 26:26 Infinite mixtures for multi-relational categorical dataInfinite mixtures for multi-relational categorical data Janne Sinkkonen Janne Sinkkonen	[syn] 3126 views, 26:05 Markov Logic Improves Protein β-Partners PredictionMarkov Logic Improves Protein β-Partners Prediction Marco Lippi Marco Lippi	[syn] 4870 views, 21:33 A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure DiscoveryA Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery Arthur Gretton Arthur Gretton 1 comment