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Aaron McDaid
Researcher at University of Lausanne
Publications - 36
Citations - 1813
Aaron McDaid is an academic researcher from University of Lausanne. The author has contributed to research in topics: Community structure & Cluster analysis. The author has an hindex of 18, co-authored 34 publications receiving 1513 citations. Previous affiliations of Aaron McDaid include University Hospital of Lausanne & University College Dublin.
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Proceedings Article
Detecting highly overlapping community structure by greedy clique expansion
TL;DR: GCE is the only algorithm to perform well on these synthetic graphs, in which every node belongs to multiple communities, and when put to the task of identifying functional modules in protein interaction data, and college dorm assignments in Facebook friendship data, the algorithm performs competitively.
Posted Content
Detecting highly overlapping community structure by greedy clique expansion
TL;DR: Greedy Clique Expansion (GCE) as mentioned in this paper identifies distinct cliques as seeds and expands these seeds by greedily optimizing a local fitness function, which is the only algorithm to perform well on these synthetic graphs.
Posted Content
Normalized Mutual Information to evaluate overlapping community finding algorithms
TL;DR: A measure based on normalized mutual information, which demonstrates unintuitive behaviour of this measure, and shows how this can be corrected by using a more conventional normalization, is demonstrated.
Proceedings ArticleDOI
Detecting Highly Overlapping Communities with Model-Based Overlapping Seed Expansion
Aaron McDaid,Neil Hurley +1 more
TL;DR: This paper presents a scalable algorithm, MOSES, based on a statistical model of community structure, which is capable of detecting highly overlappingcommunity structure, especially when there is variance in the number of communities each node is in.
Journal ArticleDOI
Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models
TL;DR: Model-based clustering using a family of Gaussian mixture models, with parsimonious factor analysis-like covariance structure, is described and an ecient algorithm for its implementation is presented, showing its eectiveness when compared to existing software.