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    English

      ArticleRank

      ✓ File Writeback ✓ Property Writeback ✓ Direct Return ✓ Stream Return ✕ Stats

      Overview

      ArticleRank has been derived from PageRank to measure the influence of journal articles.

      Concepts

      ArticleRank

      Similar to links between webpages, citations between articles (books, reports, etc.) represent authoritativeness and high quality. It is normally assumed that the greater the number of citations that an article receives, the greater impact that article has within its particular research area.

      However, not all articles are equally important. Hence, this approach based on PageRank was proposed to rank articles.

      ArticleRank retains the basic PageRank methodology while making some modifications. When an article passes its rank among its forward links, it does not divide the rank equally by the out-degree of that article, but by the sum of the out-degree of that article and the average out-degree of all articles. The rank of article u after one iteration is:

      where Bu is the backlink set of u, d is the damping factor. This change of the denominator reduces the bias that an article with very small out-degree makes a greater contribution to its forward links.

      The denominator of Ultipa's ArticleRank is different from the original paper while the core idea is the same.

      Considerations

      In comparison with WWW, some features have to be considered for citation networks, such as:

      • An article cannot cite itself, i.e., there is no self-loop in the network.
      • Two articles cannot cite each other, i.e., an article cannot be both the forward link and the backlink of another article.
      • The citations in a published article will not change, i.e., the forward links of an article is fixed.

      Syntax

      • Command: algo(page_rank)
      • Parameters:
      Name
      Type
      Spec
      Default
      Optional
      Description
      init_value float >0 0.2 Yes The same initial rank for all nodes
      loop_num int >=1 5 Yes Number of iterations
      damping float (0,1) 0.8 Yes Damping factor
      weaken int 1, 2 1 No For ArticleRank, keep it as 2; 1 means to run PageRank
      limit int ≥-1 -1 Yes Number of results to return, -1 to return all results
      order string asc, desc / Yes Sort nodes by the rank

      Examples

      The example graph is as follows:

      File Writeback

      Spec Content
      filename _id,rank
      algo(page_rank).params({
        init_value: 1,
        loop_num: 50,
        damping: 0.8,
        weaken: 2,
        order: 'desc'
      }).write({
          file: {filename: 'rank'}
      })
      

      Results: File rank

      book4,0.428308
      book5,0.375926
      book6,0.319926
      book7,0.2
      book3,0.2
      book2,0.2
      book1,0.2
      

      Property Writeback

      Spec Content Write to Data Type
      property rank Node property float
      algo(page_rank).params({
        loop_num: 50,
        weaken: 2
      }).write({
        db:{property: 'AR'}
      })
      

      Results: Rank for each node is written to a new property named AR

      Direct Return

      Alias Ordinal Type Description Columns
      0 []perNode Node and its rank _uuid, rank
      algo(page_rank).params({
        init_value: 1,
        loop_num: 50,
        damping: 0.8,
        weaken: 2,
        order: 'desc',
        limit: 3
      }) as AR 
      return AR
      

      Results: PR

      _uuid rank
      4 0.42830801
      5 0.37592599
      6 0.31992599

      Stream Return

      Alias Ordinal Type Description Columns
      0 []perNode Node and its rank _uuid, rank
      algo(page_rank).params({
        loop_num: 50,
        damping: 0.8,
        weaken: 2,
        order: 'desc',
        limit: 3
      }).stream() as AR 
      find().nodes({_uuid == AR._uuid}) as nodes
      return table(nodes._id, AR.rank)
      

      Results: table(nodes._id, AR.rank)

      nodes._id AR.rank
      book4 0.42830801
      book5 0.37592599
      book6 0.31992599
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