# Frank Emmert Streib

## Publications

##### 6 Towards Clustering of Web-based Document Structures

**Authors:**
Matthias Dehmer,
Frank Emmert Streib,
Jürgen Kilian,
Andreas Zulauf

**Abstract:**

**Keywords:**
Web Structure Mining,
Clustering Methods,
graph similarity,
graph-based patterns,
hypertext structures

##### 5 Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series

**Authors:**
Frank Emmert Streib,
Matthias Dehmer,
Gökhan H. Bakır,
Max Mühlhauser

**Abstract:**

**Keywords:**
Gene Networks,
microarray data,
Markov Chain Monte Carlo,
dynamic Bayesian networks,
structure learning

##### 4 Measuring the Structural Similarity of Web-based Documents: A Novel Approach

**Authors:**
Matthias Dehmer,
Frank Emmert Streib,
Alexander Mehler,
Jürgen Kilian

**Abstract:**

Most known methods for measuring the structural similarity of document structures are based on, e.g., tag measures, path metrics and tree measures in terms of their DOM-Trees. Other methods measures the similarity in the framework of the well known vector space model. In contrast to these we present a new approach to measuring the structural similarity of web-based documents represented by so called generalized trees which are more general than DOM-Trees which represent only directed rooted trees.We will design a new similarity measure for graphs representing web-based hypertext structures. Our similarity measure is mainly based on a novel representation of a graph as strings of linear integers, whose components represent structural properties of the graph. The similarity of two graphs is then defined as the optimal alignment of the underlying property strings. In this paper we apply the well known technique of sequence alignments to solve a novel and challenging problem: Measuring the structural similarity of generalized trees. More precisely, we first transform our graphs considered as high dimensional objects in linear structures. Then we derive similarity values from the alignments of the property strings in order to measure the structural similarity of generalized trees. Hence, we transform a graph similarity problem to a string similarity problem. We demonstrate that our similarity measure captures important structural information by applying it to two different test sets consisting of graphs representing web-based documents.

**Keywords:**
Web Structure Mining,
hypertext,
graph similarity,
generalized trees,
hierarchical and directed graphs

##### 3 Protein Graph Partitioning by Mutually Maximization of cycle-distributions

**Authors:**
Frank Emmert Streib

**Abstract:**

**Keywords:**
Graph Partitioning,
unweighted graph,
protein domains

##### 2 Application of a Similarity Measure for Graphs to Web-based Document Structures

**Authors:**
Matthias Dehmer,
Frank Emmert Streib,
Alexander Mehler,
Jürgen Kilian,
Max Mühlhauser

**Abstract:**

**Keywords:**
Web Structure Mining,
hypertext,
graph similarity,
generalized trees,
hierarchical and directed graphs

##### 1 A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer

**Authors:**
Frank Emmert Streib,
Matthias Dehmer,
Jing Liu,
Max Mühlhauser

**Abstract:**

**Keywords:**
Cancer,
graph similarity,
DNA microarray data