What are the ways to transfer a graph from one Relation space to a Euclidean space with less time complexity? although there are some ways solution (such as signal process, spectral method ), they have a high time complexity.
I do not have the experience to deal with this transformation. Does it mean GNN? Can partial information be convered? Since global information may be little big.
I want to convert a graph from Relation special from to Euclidean space, in order to use of clustering algorithm in the graph. Clustering algorithms can be used in Euclidean space, They are not used directly in graphs.
I can suggest you the following paper, which is utilizing DBSCAN algorithm for the sake of community detection and it is really sweet!
Zhou, Xu, et al. "A density based link clustering algorithm for overlapping community detection in networks." Physica A: Statistical Mechanics and its Applications 486 (2017): 65-78.
Good luck.
Dear Sayyed-Ahmad Naghavi-Nozad,
thank you so much for sharing the good paper.
Dear Kamal,
It is your most welcome. Hope it will be of good use.
Best of luck ...
I suggest you follow Preprint Multi-relational Poincar\'e Graph Embeddings
See also this discussion: https://www.researchgate.net/post/Can_someone_educate_me_on_how_to_convert_graphs_into_vector_spaces
Dear Muhammad Ali,
Do you agree with me that graph embedding algorithms are one of the best solutions to this problem?
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