https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 M-Link: a link clustering memetic algorithm for overlapping community detection https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:40778 Wed 31 Aug 2022 10:24:11 AEST ]]> A memetic algorithm for community detection by maximising the connected cohesion https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32234 Tue 15 May 2018 14:22:14 AEST ]]> Datasets for business and consumer analytics https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45986 Tue 08 Nov 2022 14:40:22 AEDT ]]> Overlapping communities in co-purchasing and social interaction graphs: a memetic approach https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45982 Tue 08 Nov 2022 14:33:11 AEDT ]]> The cohesion-based communities of symptoms of the largest component of the DSM-IV network https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:48535 Diagnostic and Statistical Manuals of Mental Disorders “DSM-IV” and the most recent addition, DSM-5 allow us to introspect, using the solution provided by modern algorithms, if there exists a consensus between the clusters obtained via a data-driven approach, with the current classifications. In the case of mental disorders, the availability of a follow-up consensus collection (e.g. in this case the DSM-5), potentially allows investigating if the classification of disorders has moved closer (or away) to what a data-driven analytic approach would have unveiled by objectively inferring it from the data of DSM-IV. In this contribution, we present a new type of mathematical approach based on a global cohesion score which we introduce for the first time for the identification of communities of symptoms. Different from other approaches, this combinatorial optimization method is based on the identification of “triangles” in the network; these triads are the building block of feedback loops that can exist between groups of symptoms. We used a memetic algorithm to obtain a collection of highly connected-cohesive sets of symptoms and we compare the resulting community structure with the classification of disorders present in the DSM-IV.]]> Thu 02 May 2024 14:32:58 AEST ]]> Identifying communities of trust and confidence in the charity and not-for-profit sector: a memetic algorithm approach https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:18758 Sat 24 Mar 2018 08:02:46 AEDT ]]> MA-Net: a reliable memetic algorithm for community detection by modularity optimization https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:23060 Sat 24 Mar 2018 07:12:28 AEDT ]]> Overlapping community detection in complex networks with memetic algorithms https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33153 Mon 23 Sep 2019 10:41:03 AEST ]]> Memetic algorithms for community detection and clustering problems https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27908 Fri 10 Aug 2018 15:19:56 AEST ]]> United in criticism: The discursive politics and coalitions of Australian energy debates on social media https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54703 Fri 08 Mar 2024 12:14:51 AEDT ]]>