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A Query-Driven System for Discovering Interesting Subgraphs in Social Media
[article]
2021
arXiv
pre-print
Social media data are often modeled as heterogeneous graphs with multiple types of nodes and edges. We present a discovery algorithm that first chooses a "background" graph based on a user's analytical interest and then automatically discovers subgraphs that are structurally and content-wise distinctly different from the background graph. The technique combines the notion of a group-by operation on a graph and the notion of subjective interestingness, resulting in an automated discovery of
arXiv:2102.09120v1
fatcat:wn2gl6krvzb3xlkvn7u3kqieaq