Work place: IT Division, NSIT, University of Delhi, India
E-mail: ambikag44@gmail.com
Website:
Research Interests: Data Mining, Data Compression, Data Structures and Algorithms
Biography
Ambika is an undergraduate student in Netaji Subhas Institute of Technology (NSIT). NSIT is affiliated to Delhi University, India. She has keen interest in data mining.
By Hardeo Kumar Thakur Anand Gupta Bhavuk Jain Ambika
DOI: https://doi.org/10.5815/ijitcs.2017.04.07, Pub. Date: 8 Apr. 2017
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.
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