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With review graphs [39] the method to detect spam reviewers using a heterogeneous graph of product, review and the reviewer proposed by Wang et al turned the direction towards detecting relationships between these three nodes with parameters such as trustworthiness of the reviewers, reliability of the stores and honesty of reviews using the data from resellerratings.com.com
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Our method uses a heterogeneous graph consisting of three sub-graphs connected to each other.
We used a heterogeneous dataset for inferring the predictors.
We could now compute the similarity between the representative graphs using a graph kernel.
These results also show that even for graph data, using a graph database is not necessarily an advantage.
We directly define our graph by using a bidirected graph.
The interaction was interpreted using a line graph.
Our approach is designed to address a wide variety of industry concerns as it achieves substantial data compression by storing only essential FE information and is efficient for visualizing heterogeneous analytic results by using a modified scene graph data structure.
We addressed this problem using a graph theoretical approach.
The wound areas recorded were measured using a graph paper.
The major reason for us to use a graph database was their support for graph algorithms.
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