Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: User Profile Relationships using String Similarity Metrics in Social
  Networks   This article reviews the problem of degree of closeness and interaction level
in a social network by ranking users based on similarity score. This similarity
is measured on the basis of social, geographic, educational, professional,
shared interests, pages liked, mutual interested groups or communities and
mutual friends. The technique addresses the problem of matching user profiles
in its globality by providing a suitable matching framework able to consider
all profiles' attributes and finding the similarity by new ways of string
metrics. It is able to discover the biggest possible number of profiles that
are similar to the target user profile, which the existing techniques are
unable to detect. Attributes were assigned weights manually; string and
semantic similarity metrics were used to compare attributes values thus
predicting the most similar profiles. Profile based similarity show the exact
relationship between users and this similarity between user profiles reflects
closeness and interaction between users.

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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
2
The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci