Graph drawing by force-directed placement
Software—Practice & Experience
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Efficient string matching: an aid to bibliographic search
Communications of the ACM
KeyGraph: Automatic Indexing by Co-occurrence Graph based on Building Construction Metaphor
ADL '98 Proceedings of the Advances in Digital Libraries Conference
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A tutorial on spectral clustering
Statistics and Computing
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
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We analysed the community structure of a network of news clips where relationships were established by the co-reference of entities in pairs of clips. Community detection was applied to a unidimensional version of the news clips network, as well as to a multidimensional version where dimensions were defined based on three different classes of entities: places, people, and dates. The goal was to study the impact on the quality of the identified community structure when using multiple dimensions to model the network. We did a two-fold evaluation, first based on the modularity metric and then based on human input regarding community semantics. We verified that the assessments of the evaluators differed from the results provided by the modularity metric, pointing towards the relevance of the utility and network integration phases in the identification of semantically cohesive groups of news clips.