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Probabilistic Inference for Graph Classification

author:Koji Tsuda, Max Planck Institute for Biological Cybernetics, Max Planck Institute
published: Feb. 25, 2007,   recorded: June 2006,   views: 273
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Slides

Slides
0:01 Graph Mining Applications in Machine Learning Problems
2:28 Motivations for graph analysis
3:28 Graphs
4:07 Graph Structures in Biology
5:00 Overview
6:32 Path Representations & Marginalized Graph Kernels
6:35 Marginalized Graph Kernels
7:57 Label Path
9:05 Path-Probability Vector
9:48 Kernel Definition
11:37 Computation
13:35 Graph Kernel Applications
14:33 Strong Points of MGK
15:20 Drawbacks of Graph Kernels
18:06 Substructure Representation & Graph Mining
18:16 Substructure Representation
20:31 Graph Mining pt 1
21:59 Graph Mining pt 2
23:15 Enumeration on Tree-Shaped Search Space
24:52 Tree Pruning
26:30 Gspan
27:02 Depth First Search (DFS) Code
27:47 Discriminative Patterns
29:21 Multiclass Version
30:13 Summary of Graph Mining
30:58 EM-Based Clustering of Graphs (Tsudaand Kudo, ICML 2006)
31:12 EM-Based Graph Clustering
31:57 Probabilistic Model
32:45 Ordinary EM Algorithm
33:58 Regularization
35:36 E-Step
36:27 M-Step
37:28 Solution pt 1
38:18 Solution pt 2
38:22 Important Observation
38:57 Mining for Active Patterns
39:45 Experiments - RNA Graphs
41:12 Clustering RNA Graphs
42:14 Examples of RNA Graphs
42:43 ROC Scores
44:24 No of Patterns & Time
45:37 Found Patterns
46:23 Conclusion
46:58 Ongoing Work
50:20 Multiclass Version (a)

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Description

Graph data is getting increasingly popular in, e.g., bioinfor- matics and text processing. A main dificulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible sub- graphs, the dimensionality gets too large for usual statistical methods.

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