Authors:
James Abello
1
;
2
;
Timothy Tangherlini
3
and
Haoyang Zhang
2
Affiliations:
1
DIMACS, Rutgers, The State University of New Jersey, 110 Frelinghuysen Rd, Piscataway, New Jersey, U.S.A.
;
2
Department of Computer Science, Rutgers, The State University of New Jersey, 110 Frelinghuysen Rd, Piscataway, New Jersey, U.S.A.
;
3
Department of Scandinavian, University of California, Berkeley, 6303 Dwinelle Hall, California, U.S.A.
Keyword(s):
Social Media, Graph Decompositions, Max Flow Min Cut, Visualization.
Abstract:
Viewing social media posts as a collection of directed triples { ⟨ Entity, Verb, Entity ⟩ } provides a frequency labeled graph with vertices comprising the set of entities, and each edge encoding the frequency of co-occurrence of the pair of entities labeled by its linking verb or verb phrase. The set of edges of the underlying topology can be partitioned into maximal subgraphs, called fixed points, each consisting of a sequence of vertex disjoint layers. We exploit this view to observe how information spreads on social media platforms. This is achieved via traces of label propagation across a Max Flow Min Cut decomposition of each fixed point. These traces generate a weighted label set system with an underlined label distribution, from which we derive a barycentric coordinatization of the collection of minimum cuts of each fixed point. This is a novel graph decomposition that incorporates information flow with a multi-layered summary of noisy social media forums, providing a compreh
ensible yet fine-grained summary of social media conversations.
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