An efficient and principled method for detecting communities in networks

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl

The Global Edge: An Agenda for Chicago's Future

Examines the challenges the city faces in sustaining economic vitality, and lays out the priorities for the next two decades: improve transportation and infrastructure, build human capital, and increase global engagement

DISCOVERING DRIVER MUTATIONS IN BIOLOGICAL DATA

Background Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations. Results We propose QuaDMutEx an QuadMutNetEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. QuaDMutNetEx is our proposed method that combines protein-protein interaction networks to the method elements of QuaDMutEx. In particular, QuaDMutEx incorporates three novel elements: a non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty. In the new method, we incorporated a new quadratic rewarding term that prefers gene solution set that is connected with respect to protein-protein interaction networks. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors. Conclusions Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx and QuaDMutNetEx are tools that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods

An Empirical Study of Branding Strategy at Dealer point for Selling of Car-a qualitative & systematic Review of Literature

India is one of the world’s fastest growing automobile markets and is poised to become the third largest passenger’s car market by 2020 (Philip, L. 2016, Economic Times). The recorded sales growth of 4 wheelers like passenger car & utility vehicle has also risen up to 7.87 % and 6.25% respectively during April-March 2016 (SIAM, 2015-16). But what makes a car maker like Japan’s Maruti Suzuki and Korea’s Hyundai enjoys more than 67% of market share while others like US car makers Ford India and General Motors combined market share is just 4-5%(Philip,L.2016,The Economic Times). Sales in the North & East region have evidenced only 5%of changes in the FY16 which is comparatively lower than the west & south region (Khan,A.N,2016, The Economic Times). The Japanese car makers(Honda, Hyundai, Isuzu Motors, Nissan &Toyota) achieved an average of 48.01% of growth till July 2016 having a better stand from the Indian car makers (Hindustan Motors, M&M,M&S, Tata & Force motors) i.e. 6.74% (Autocar Pro News Desk, July 2016). In this study the researcher explored the factors affecting the satisfaction of prospective car buyers and existing car users at dealer point and facilitate dealer to create a brilliant “moment of truth” (Pioneered by JanCarlzon) when a customer encounter with company.(Madge, Davidson & Beaujean, 2006

Network Data Mining: Methods and techniques for discovering deep linkage between attributes

Abstract. Network Data Mining identifies emergent networks between myriads of individual data items and utilises special algorithms that aid visualisation of ‘emergent ’ patterns and trends in the linkage. It complements conventional data mining methods, which assume the independence between the attributes and the independence between the values of these attributes. These techniques typically flag, alert or alarm instances or events that could represent anomalous behaviour or irregularities because of a match with pre-defined patterns or rules. They serve as ‘exception detection ’ methods where the rules or definitions of what might constitute an exception are able to be known and specified ahead of time. Many problems are suited to this approach. Many problems however, especially those of a more complex nature, are not well suited. The rules or definitions simply cannot be specified. For example, in the analysis of transaction data there are no known suspicious transactions. This chapter presents a human-centred network data mining methodology that addresses the issues of depicting implicit relationships between data attributes and/or specific values of these attributes. A case study from the area of security illustrates the application of the methodology and corresponding data mining techniques. The chapter argues that for many problems, a ‘discovery’ phase in the investigative process based on visualisation and human cognition is a logical precedent to, and complement of, more automated ‘exception detection ’ phases