Abstract
Time evolving networks tend to have an element of regularity. This regularity is characterized by existence of repetitive patterns in the data sequences of the graph metrics. As per our research, the relevance of such regular patterns to the network has not been adequately explored. Such patterns in certain data sequences are indicative of properties like popularity, activeness etc. which are of vital significance for any network. These properties are closely indicated by data sequences of graph metrics - degree prestige, degree centrality and occurrence. In this paper, (a) an improved mining algorithm has been used to extract regular patterns in these sequences, and (b) a methodology has been proposed to quantitatively analyse the behavior of the obtained patterns. To analyze this behavior, a quantification measure coined as "Sumscore" has been defined to compare the relative significance of such patterns. The patterns are ranked according to their Sumscores and insights are then drawn upon it. The efficacy of this method is demonstrated by experiments on two real world datasets.
Highlights
Real life networks are increasingly being modeled as graphs
The relevance of such regular patterns to the network has not been adequately explored. Such patterns in certain data sequences are indicative of properties like popularity, activeness, etc., which are of vital significance for any network. These properties are closely indicated by data sequences of graph metrics – degree prestige, degree centrality, and occurrence
We look for regular patterns in the degree prestige and centrality sequences for nodes and in occurrence sequences for edges
Summary
Real life networks are increasingly being modeled as graphs. Graphs provide an excellent representation of interconnections amongst the nodes of a network. To the best of our knowledge, no further work has been done in applying said approaches for regular patterns to analyze a dynamic graph for the extraction of behavioral information. This information can be the statistics of a mobile user’s activity over the network, the popularity of a person among his peers, or the distribution of air-traffic on a route through the year, etc. Three data sequences of –occurrence, degree centrality,and degree prestige have been chosen as the best suited for the purpose of behavioral analysis by the authors These sequences are first mined for regular patterns for different entities such as nodes and edges.
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