2013 IEEE 11th International Conference on Dependable, Autonomic and Secure Computing 2013
DOI: 10.1109/dasc.2013.108
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Graph-Based Friend Recommendation in Social Networks Using Artificial Bee Colony

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Cited by 15 publications
(11 citation statements)
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“…ABC algorithm is used to learn the graph weights by optimizing four parameters. The method is compared to classic machine learning algorithms such as K-nearest Neighbor, Support Vector Machine and Multilayer perceptron, and it yielded an accuracy of 77% [11] .…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…ABC algorithm is used to learn the graph weights by optimizing four parameters. The method is compared to classic machine learning algorithms such as K-nearest Neighbor, Support Vector Machine and Multilayer perceptron, and it yielded an accuracy of 77% [11] .…”
Section: Related Workmentioning
confidence: 99%
“…A degree of a vertex represents the number of edges connected to the vertex. Finally, the density of the graph can be calculated using equation (11) because the initial graph is undirected. The density is computed by dividing the number of existent edges (379776) by the total number of vertices (n * (n-1)), which is 1538840, with a result that equals to 0.247.…”
Section: Figure 7-pcc Between Cosine Similarity and Euclideanmentioning
confidence: 99%
“…The ABC selects the appropriate auxiliary materials in terms of their difficulty, number of 'likes', and course topics. That in [12] developed a friend recommendation system using an ABC exploiting structural properties and topological features of social networks. It first generates a subgraph of the network of the user and then suggests new links considering the parameters mentioned by optimizing the relative importance of the weights for these parameters using ABC.…”
Section: Pcc Ejc Vcsmentioning
confidence: 99%
“…The work in [11] optimized clusters of users with ABC to avoid local optima. Similarly, topological features of social networks were exploited using the ABC algorithm for friend recommendations in [12]. More approaches are detailed in Section 2.…”
Section: Introductionmentioning
confidence: 99%
“…However it cant be used friend recommendation. Traditional personalized friend recommendation always uses static attribute information or topological structure information [1,2]. Those kinds of information are static and they are not sufficient conditions for friend recommendation.…”
Section: Introductionmentioning
confidence: 99%