[HN Gopher] Using Topology to Classify Labelled Graphs
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       Using Topology to Classify Labelled Graphs
        
       Author : Topolomancer
       Score  : 30 points
       Date   : 2020-12-02 18:46 UTC (4 hours ago)
        
 (HTM) web link (bastian.rieck.me)
 (TXT) w3m dump (bastian.rieck.me)
        
       | chartpath wrote:
       | This is a really cool and very smart looking mathematical way of
       | looking at something that I do with lower-tech taxon labels using
       | LTREE in Postgres to store the annotations.
       | 
       | In PG you can do a kind of KNN for things that are X LTREE
       | degrees away as well as ordering by distance from trigrams of
       | text. With an ensemble of online learning models for different
       | blobs of taxa, it is a way to prevent concept drift and give
       | weights to the different subdomains of training data to fight
       | bias.
       | 
       | This kind of topological approach is great. One challenge in SQL
       | is that class membership can only be so expressive and I have a
       | nagging desire for slightly more RDF-like edges of
       | predicates/verbs, but that still feels like overkill to me since
       | I can already do rule engines with facts based on the labels.
       | Therefore I end up with class names like "things that verb" or
       | "things that are verbable" like would be done for naming
       | interfaces.
       | 
       | Scanning the paper I don't understand the math well enough (am
       | more of a code plumber). How would different "types" of edges be
       | encoded to get past the perceived limitations of class
       | membership?
        
       | nerdponx wrote:
       | Machine learning (and more generally modeling) on graphs seems
       | really powerful. Interesting to see continued progress in this
       | area.
        
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       (page generated 2020-12-02 23:01 UTC)