Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Structural analysis of hypertexts: identifying hierarchies and useful metrics
ACM Transactions on Information Systems (TOIS)
Directed hypergraphs and applications
Discrete Applied Mathematics - Special issue: combinatorial structures and algorithms
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Relationally encoded links and the rhetoric of hypertext
HYPERTEXT '87 Proceedings of the ACM conference on Hypertext
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Computational Linguistics
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Evolution and Structure of the Internet: A Statistical Physics Approach
Evolution and Structure of the Internet: A Statistical Physics Approach
GXL: a graph-based standard exchange format for reengineering
Science of Computer Programming - Software analysis, evolution and re-engineering
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Model-aware Wiki analysis tools: the case of HistoryFlow
Proceedings of the 6th International Symposium on Wikis and Open Collaboration
Computer Speech and Language
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
On the self-similarity of intertextual structures in Wikipedia
Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research
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This article elaborates a framework for representing and classifying large complex networks by example of wiki graphs. By means of this framework we reliably measure the similarity of document, agent, and word networks by solely regarding their topology. In doing so, the article departs from classical approaches to complex network theory which focuses on topological characteristics in order to check their small world property. This does not only include characteristics that have been studied in complex network theory, but also some of those which were invented in social network analysis and hypertext theory. We show that network classifications come into reach which go beyond the hypertext structures traditionally analyzed in web mining. The reason is that we focus on networks as a whole as units to be classified—above the level of websites and their constitutive pages. As a consequence, we bridge classical approaches to text and web mining on the one hand and complex network theory on the other hand. Last but not least, this approach also provides a framework for quantifying the linguistic notion of intertextuality.