Cite this DOI

Bindu, T. H. (2017). Deducing Private Information from Social NetworkUsing Unified Classification. *Journal of Science & Technology*, *02*(03), 41–46. https://doi.org/10.46243/jstj.2017.v2.i3.145

@article{bindu2017deducing,
  author    = {Bindu, T Hima},
  title     = {{Deducing Private Information from Social NetworkUsing Unified Classification}},
  journal   = {Journal of Science \& Technology},
  year      = {2017},
  month     = {may},
  volume    = {02},
  number    = {03},
  pages     = {41--46},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jstj.2017.v2.i3.145},
  url       = {https://doi.org/10.46243/jstj.2017.v2.i3.145},
  language  = {en},
  abstract  = {Online social networks are used by many people. These Social networks allowtheir users to connect bymeans of various link types in which the network gives an opportunity for people to list details about themselves that are relevant to the nature of the network. Here there is a chance of inference when user released some personal information in the network. Social network is represented as graph structure in which nodes and edges denotes user’s of network and relationship links with friends. In this paper, the social network data has been classified with the help of collective classification (both node and link classification) method. Using the collective classification method the system could infer more sensitive information from the network with high accuracy. In collective classification method, it involves three components called local classifier, relational classifier and collective inference. From this experiments conducted in this research work, it is observed that the proposed work provide better classification accuracy due to the application of collective classification method in link analysis.}
}

TY  - JOUR
TI  - Deducing Private Information from Social NetworkUsing Unified Classification
AU  - Bindu, T Hima
JO  - Journal of Science & Technology
PY  - 2017
DA  - 2017/05/11/
VL  - 02
IS  - 03
SP  - 41
EP  - 46
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Online social networks are used by many people. These Social networks allowtheir users to connect bymeans of various link types in which the network gives an opportunity for people to list details about themselves that are relevant to the nature of the network. Here there is a chance of inference when user released some personal information in the network. Social network is represented as graph structure in which nodes and edges denotes user’s of network and relationship links with friends. In this paper, the social network data has been classified with the help of collective classification (both node and link classification) method. Using the collective classification method the system could infer more sensitive information from the network with high accuracy. In collective classification method, it involves three components called local classifier, relational classifier and collective inference. From this experiments conducted in this research work, it is observed that the proposed work provide better classification accuracy due to the application of collective classification method in link analysis.
DO  - 10.46243/jstj.2017.v2.i3.145
UR  - https://doi.org/10.46243/jstj.2017.v2.i3.145
ER  -

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    "DOI": "10.46243/jstj.2017.v2.i3.145",
    "URL": "https://doi.org/10.46243/jstj.2017.v2.i3.145",
    "title": "Deducing Private Information from Social NetworkUsing Unified Classification",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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            "family": "Bindu",
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    "issue": "03",
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Online social networks are used by many people. These Social networks allowtheir users to connect bymeans of various link types in which the network gives an opportunity for people to list details about themselves that are relevant to the nature of the network. Here there is a chance of inference when user released some personal information in the network. Social network is represented as graph structure in which nodes and edges denotes user’s of network and relationship links with friends. In this paper, the social network data has been classified with the help of collective classification (both node and link classification) method. Using the collective classification method the system could infer more sensitive information from the network with high accuracy. In collective classification method, it involves three components called local classifier, relational classifier and collective inference. From this experiments conducted in this research work, it is observed that the proposed work provide better classification accuracy due to the application of collective classification method in link analysis.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjstj.2017.v2.i3.145 gives all four in one JSON answer.