Similarity in temporal graphs (Master thesis)
In all modern networks, calculating metrics and ranking nodes according to their inherent characteristics is a vital part of their analysis. It helps by providing information of the structure of the network, the association and importance of nodes, as well as presenting characteristics and information of the network itself. The main problem lies in redefining already existent models and algorithms that are built for static networks to also apply their calculations, effectively, on networks that evolve over time. This must not be done by completely excluding node structure or other information they already include but by extending them to also calculate temporal metrics. Such a change is essential due to the amount of data being extracted everyday whose components include rich temporal information. This work focuses on altering distance, diameter and centrality metrics as well as the SimRank algorithm to fit temporal data extracted from two different datasets restructured in three different graph implementations; an undirected, a directed and a bipartite one. The resulting definitions, models and implementations are analyzed and ranking algorithms were used to evaluate their significance.
|Institution and School/Department of submitter:||Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Η/Υ & Πληροφορικής|
|Subject classification:||Graphic methods|
|Keywords:||Ομοιότητα,Γραφήματα,Χρονικά γραφήματα,Μετρικές,Similarity,Graphs,Temporal graphs,Metrics|
|Appears in Collections:||Διατριβές Μεταπτυχιακής Έρευνας (Masters)|
Files in This Item:
|Μ.Ε. ΚΟΥΡΣΟΥΜΗΣ ΑΝΤΩΝΙΟΣ 2017.pdf||250.1 kB||Adobe PDF||View/Open|
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