Discovering Interesting Subgraphs in Social Media Networks

Subhasis Dasgupta; Amarnath Gupta

doi:10.1109/asonam49781.2020.9381293

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arXiv · 2009.05853

Discovering Interesting Subgraphs in Social Media Networks

Subhasis Dasgupta·Amarnath Gupta

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Abstract

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 \texttt{group-by} operation on a graph and the notion of subjective interestingness, resulting in an automated discovery of interesting subgraphs. Our experiments on a socio-political database show the effectiveness of our technique.

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Subhasis Dasgupta, Amarnath Gupta. 2020-09-12. Discovering Interesting Subgraphs in Social Media Networks. https://doi.org/10.1109/asonam49781.2020.9381293

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