Comparing and fusing terrain network information

Authors:
Emmanuel Navarro;Bruno Gaume;Henri Prade
Affiliations:
IRIT, Université de Toulouse III, Toulouse Cedex 9, France;CLLE-ERSS, Université de Toulouse II, Toulouse Cedex 9, France;CLLE-ERSS, Université de Toulouse II, Toulouse Cedex 9, France
Venue:
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Year:
2012

Citing 11
Cited 0

Perron-Frobenius theory: a new proof of the basics

Perron-Frobenius theory: a new proof of the basics
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching

SIAM Review
Substructure similarity search in graph databases

Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Closure-Tree: An Index Structure for Graph Queries

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
The link-prediction problem for social networks

Journal of the American Society for Information Science and Technology
TALE: A Tool for Approximate Large Graph Matching

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Graph Comparison Using Fine Structure Analysis

SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Invariants and variability of synonymy networks: self mediated agreement by confluence

TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Survey: Graph clustering

Computer Science Review
Semi-automatic enrichment of crowdsourced synonymy networks: the WISIGOTH system applied to Wiktionary

Language Resources and Evaluation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Terrain networks (or complex networks) is a type of relational information that is encountered in many fields. In order to properly answer questions pertaining to the comparison or to the merging of such networks, a method that takes into account the underlying structure of graphs is proposed. The effectiveness of the method is illustrated using real linguistic data networks and artificial networks, in particular.