Jan 14, 2021
Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules. Lim et al, Front Genet. 2020; 11: 603264.
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Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules. Lim et al, Front Genet. 2020; 11: 603264.
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HiDeF: identifying persistent structures in multiscale 'omics data. Zheng, et al., Genome biology doi: 10.1186/s13059-020-02228-4.
Read the full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=49990 DOI: 10.4236/jsea.2014.710078 Author(s) Nathan Aston, Jacob Hertzler, Wei Hu ABSTRACT Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Propagation Algorithm (SLPA) called SLPA Dynamic (SLPAD). This algorithm, tested on two real dynamic networks, cuts down on the time that it would take SLPA to run, as well as produces similar, and in some cases better, communities. We compared SLPAD to SLPA, LabelRankT, and another algorithm we developed, Dynamic Structural Clustering Algorithm for Networks Overlapping (DSCAN-O), to further test its validity and ability to detect overlapping communities when compared to other community detection algorithms.eww140929gjr SLPAD proves to be faster than all of these algorithms, as well as produces communities with just as high modularity for each network. KEYWORDS Community Detection, Modularity, Dynamic Networks, Overlapping Community Detection, Label Propagation
Figure 1 represents people purchasing tickets for a series of events in Boulder over a four and a half year period.
The larger circles represent the events where size reflects the number of ticket purchasers. The smaller circles are the purchasers themselves and they provide common threads between the events.
Time generally progresses from left to right and colors are gradated based on the number of common purchasers between events.
In general we see that the earlier five events all have a similar group of overlapping ticket purchasers. As time progresses we see a shift to the deep blue of the last two events.
Figure 2 is where the groups in Figure 1 have been collapsed and the arrow weight reflects the total common purchasers between groups. For example Event 0 heavily translates into purchasers for groups 1, 2, and 3. Groups 4, 5, and 6 have fewer purchasers in common.
It should be noted that there is about a year of missing data that represents events which probably fall between the yellow and blue groups of purchasers.
Community discovery is no trivial matter in larger networks. Many of the old school algorithms were developed for graphs containing less than a couple hundred members. This article describes some of the problems and a new solution of breaking down large networks so the old algorithms still work. We'll be evaluating this approach for detecting sub-communities in your overall community.
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