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Capturing Social Data Evolution via Graph Clustering
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ISSN: 1089-7801
Maria Giatsoglou, Aristotle University of Thessaloniki, Thessaloniki
Athena Vakali, Aristotle University of Thessaloniki, Thessaloniki
Fast and unpredictable social data evolution poses challenges in capturing user activities and complex associations. Evolving graph clustering is promising for uncovering latent users’ and content’s patterns evolution.
Index Terms:
H.2.8.m Web mining, I.5.3.a Algorithms < I.5.3 Clustering < I.5 Pattern Recognition < I Computing Methodologies, E.1.d Graphs and networks < E.1 Data Structures < E Data, H.3.3.a Clustering, H.2.8.i Mining methods and algorithms, H.2.8.d Data mining < H.2.8 Database Applications < H.2 Database Management < H Information Technology and Systems,
Citation:
Maria Giatsoglou, Athena Vakali, "Capturing Social Data Evolution via Graph Clustering," IEEE Internet Computing, 09 Feb. 2012. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/MIC.2012.24>
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