Link prediction based on contribution of neighbors
2020 ◽
Vol 31
(11)
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pp. 2050158
Keyword(s):
Link prediction based on node similarity has become one of the most effective prediction methods for complex network. When calculating the similarity between two unconnected endpoints in link prediction, most scholars evaluate the influence of endpoint based on the node degree. However, this method ignores the difference in contribution of neighbor (NC) nodes for endpoint. Through abundant investigations and analyses, the paper quantifies the NC nodes to endpoint, and conceives NC Index to evaluate the endpoint influence accurately. Extensive experiments on 12 real datasets indicate that our proposed algorithm can increase the accuracy of link prediction significantly and show an obvious advantage over traditional algorithms.
An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs
2021 ◽
Vol 11
(5)
◽
pp. 1-18
2016 ◽
Vol 30
(31)
◽
pp. 1650222
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2014 ◽
Vol 672-674
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pp. 2173-2177
Keyword(s):
An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs
2021 ◽
Vol 11
(05)
◽
pp. 1-18
2005 ◽
Vol 16
(07)
◽
pp. 1097-1105
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Keyword(s):
2021 ◽
Keyword(s):
2020 ◽
Vol 19
(2)
◽
pp. 1071
Keyword(s):
2017 ◽
Vol 28
(08)
◽
pp. 1750101
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