A Unified Community Detection, Visualization and Analysis method
| Autor: | Michel Plantié, Michel Crampes |
|---|---|
| Přispěvatelé: | Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Plantié, Michel |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society social networks Computer science Semantic interpretation FOS: Physical sciences Physics and Society (physics.soc-ph) [STAT.OT]Statistics [stat]/Other Statistics [stat.ML] computer.software_genre [SPI]Engineering Sciences [physics] Analysis method modularity Social and Information Networks (cs.SI) Community detection business.industry Other Statistics (stat.OT) [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph] Community detection social networks modularity graph unified method Computer Science - Social and Information Networks Directed graph graph unified method [STAT.OT] Statistics [stat]/Other Statistics [stat.ML] Graph Visualization Important research Statistics - Other Statistics Control and Systems Engineering [PHYS.PHYS.PHYS-SOC-PH] Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph] Physics - Data Analysis Statistics and Probability Bipartite graph The Internet Data mining business computer Data Analysis Statistics and Probability (physics.data-an) |
| Zdroj: | ADVANCES IN COMPLEX SYSTEMS ADVANCES IN COMPLEX SYSTEMS, 2014, 17 (1), ⟨10.1142/S0219525914500015⟩ |
| Popis: | Community detection in social graphs has attracted researchers' interest for a long time. With the widespread of social networks on the Internet it has recently become an important research domain. Most contributions focus upon the definition of algorithms for optimizing the so-called modularity function. In the first place interest was limited to unipartite graph inputs and partitioned community outputs. Recently bipartite graphs, directed graphs and overlapping communities have been investigated. Few contributions embrace at the same time the three types of nodes. In this paper we present a method which unifies commmunity detection for the three types of nodes and at the same time merges partitionned and overlapping communities. Moreover results are visualized in such a way that they can be analyzed and semantically interpreted. For validation we experiment this method on well known simple benchmarks. It is then applied to real data in three cases. In two examples of photos sets with tagged people we reveal social networks. A second type of application is of particularly interest. After applying our method to Human Brain Tractography Data provided by a team of neurologists, we produce clusters of white fibers in accordance with other well known clustering methods. Moreover our approach for visualizing overlapping clusters allows better understanding of the results by the neurologist team. These last results open up the possibility of applying community detection methods in other domains such as data analysis with original enhanced performances. Comment: Submitted to Advances in Complex Systems |
| Databáze: | OpenAIRE |
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