Mining Maximal Quasi Regular Patterns in Weighted Dynamic Networks

Abstract

Abstract-Interactions appearing regularly in a network may be disturbed due to the presence of noise or random occurrence of events at some timestamps. Ignoring them may devoid us from having better understanding of the networks under consideration. Therefore, to solve this problem, researchers have attempted to find quasi/quasiregular patterns in non-weighted dynamic networks. To the best of our knowledge, no work has been reported in mining such patterns in weighted dynamic networks. So, in this paper we present a novel method which mines maximal quasi regular patterns on structure (MQRPS) and maximal quasi regular patterns on weight (MQRPW) in weighted dynamic networks. Also, we have provided a relationship between MQRPW and MQRPS which facilitates in the running of the proposed method only once, even when both are required and thus leading to reduction in computation time. Further, the analysis of the patterns so obtained is done to gain a better insight into their nature using four parameters, viz. modularity, cliques, most commonly used centrality measures and intersection. Experiments on Enron-email and a synthetic dataset show that the proposed method with relationship and analysis is potentially useful to extract previously unknown vital information.

Mining for Focus and Priorities in Donald Trump and Hillary Clinton’s Daily Tweets during the 2016 U.S. General Elections

“…Researchers do, indeed, recognize that the pervasiveness of mobile devices used for communication has had mixed effects on human behaviors ( [32], [33]). Therefore, the mining of information that occur regularly on computer networked is considered a very critical task that could provide useful information [34], especially if the context of each occurrence is well ascertained [35]. Data also show that the mining research over data stream has been prominent as they can be applied in many alternative areas in the real worlds [36].…”