Mining Maximal Quasi Regular Patterns in Weighted Dynamic Networks

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Hardeo Kumar Thakur 1,* Anand Gupta 1 Bhavuk Jain 2 Ambika 2

1. COE Division, NSIT, University of Delhi, India

2. IT Division, NSIT, University of Delhi, India

* Corresponding author.


Received: 10 May 2016 / Revised: 3 Sep. 2016 / Accepted: 12 Nov. 2016 / Published: 8 Apr. 2017

Index Terms

Dynamic weighted graph, evolving graph, regular graph, quasi regular graph, frequent graph


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/quasi-regular 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.

Cite This Paper

Hardeo Kumar Thakur, Anand Gupta, Bhavuk Jain, Ambika, "Mining Maximal Quasi Regular Patterns in Weighted Dynamic Networks", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.4, pp.48-62, 2017. DOI:10.5815/ijitcs.2017.04.07


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