Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Mining Graph Data
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Dex: high-performance exploration on large graphs for information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
BIBEX: a bibliographic exploration tool based on the DEX graph query engine
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Colibri: fast mining of large static and dynamic graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Graphs from Search Engine Queries
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
A model for fast web mining prototyping
Proceedings of the Second ACM International Conference on Web Search and Data Mining
GD'04 Proceedings of the 12th international conference on Graph Drawing
| Hi-index | 0.00 |
In this paper we propose a generic model to generate basic multi-partite graphs obtained by associations found in arbitrary data. The interest of such a model is to be the formal basis behind a tool for automatic graph generation that we are developing. This tool will automatically generate the basic multi-partite graphs that represents the arbitrary data provided as input. We consider input data as collections of complex objects composed by a set or a list of heterogeneous elements. Our tool will provide an interface for the user to specify the kind of nodes that are relevant for the application domain in each case. Those nodes will be obtained from the complex input objects by simple extraction rules. The objective of this paper is to present the model to represent basic multi-partite graphs and the way to define the nodes of the graph using simple derivation rules defined by the user. In order to validate our model we give three examples of radically different data sets. Those examples come from the Web log queries, processing text collections, and bibliographic databases.