Incremental Maintenance of Maximal Cliques in a Dynamic Graph

We consider the maintenance of the set of all maximal cliques in a dynamic graph that is changing through the addition or deletion of edges. We present nearly tight bounds on the magnitude of change in the set of maximal cliques, as well as the first change-sensitive algorithms for clique maintenance, whose runtime is proportional to the magnitude of the change in the set of maximal cliques. We present experimental results showing these algorithms are efficient in practice and are faster than prior work by two to three orders of magnitude.Comment: 18 pages, 8 figure

Shared-Memory Parallel Maximal Clique Enumeration

We present shared-memory parallel methods for Maximal Clique Enumeration (MCE) from a graph. MCE is a fundamental and well-studied graph analytics task, and is a widely used primitive for identifying dense structures in a graph. Due to its computationally intensive nature, parallel methods are imperative for dealing with large graphs. However, surprisingly, there do not yet exist scalable and parallel methods for MCE on a shared-memory parallel machine. In this work, we present efficient shared-memory parallel algorithms for MCE, with the following properties: (1) the parallel algorithms are provably work-efficient relative to a state-of-the-art sequential algorithm (2) the algorithms have a provably small parallel depth, showing that they can scale to a large number of processors, and (3) our implementations on a multicore machine shows a good speedup and scaling behavior with increasing number of cores, and are substantially faster than prior shared-memory parallel algorithms for MCE.Comment: 10 pages, 3 figures, proceedings of the 25th IEEE International Conference on. High Performance Computing, Data, and Analytics (HiPC), 201

Computational Facilities and Web-Resources: Case Study of Large Private University with Fast-Growing Clients

Speed, space and judicious sharing web-related resources are the key indicators of successful management of the computing-facilities and other web-resources of any progressive organisation. Such a case becomes much more demanding for any professional academic institution, where the majority stake-holders, that is the young student-users of web-resources, are heavily dependent on web-based learning and personal communications. Other stake holders, like administrative staff, teaching and research community of universities have web-dependence, mostly for known resources. Fast growing dependence of different categories of stake-holders of such large institutes warrants a case-study research, so as to study the present pattern of uses of web-resources, including the timing and pockets of users, and then to have a sustainable strategic planning for a better resource-management of web-resources for future. The present paper is a case study of a leading private university of Odisha (in India) with over 65,000 users of ‘university web-network' and over 7500 fixed-systems, which analyses users' time-series data of last quarter and suggests a futuristic model for optimal and effective use of - ˜Institute Web-Resources and computing facilities'. It studies both fixed-line load and load-management of wireless (Wi Fi) connections, across the 25 campuses of the Institute, scattered and geographically located within 15 sq. km

Mean and Volatility Spillovers between REIT and Stocks Returns A STVAR-BTGARCH-M Model

In this study we have examined volatility spillovers as well as volatility-in-mean effect between REIT returns and stock returns for both the USA and the UK by applying a bivariate GARCH-M model where the conditional mean is specified by a smooth transition VAR model. Dynamic conditional correlation approach has been applied with the GJR-GARCH specification so that the intrinsic nature of asymmetric volatility in case of positive and negative shocks can be duly captured. The major findings that we have empirically found is that the mean spillover effect from stock returns to REIT returns is significant for both the countries while the same from REIT returns to stock returns is significant only in the UK. It is also evident from the results that own risk-return relationship of REIT market is positive and significant only in the bear market situation in both the countries while for the stock market own risk-return relationship is insignificant for both the bull and bear markets in the USA but it is negative in the bear market condition and positive in the bull market situation for the UK. We have also found that asymmetric nature of conditional variance and dynamic behavior in the conditional correlation holds as well. Finally, several tests of hypotheses regarding equality of various kinds of spillover effects in the bull and bear market situations show that these spillover effects are not the same in the two market conditions in most of the aspects considered in this study

Studies on Magnetic and Dielectric Properties of Antiferromagnetically Coupled Dinuclear Cu(II) in a One-Dimensional Cu(II) Coordination Polymer

A one-dimensional Cu(II) coordination polymer with encapsulated antiferromagnetically coupled binuclear Cu(II) has been synthesized by using 5-nitroisophthalic acid (5-N-IPA) and 4-aminopyridine (4-APY) [Cu2(5-N-IPA)2(4-APY)4] n (1). Electrical properties are examined by complex impedance (Z*), dielectric permittivity (ε*), and ac conductivity studies at different frequencies (10 kHz-5 MHz) and temperatures (253-333 K). The contribution of grain and grain boundary has been explained by a different theoretical model. The variable temperature magnetic susceptibility data for compound 1 were recorded between 300 and 2 K. The shape of the curve (χM T vs T) indicates dominant antiferromagnetic coupling, which results from the interaction between the copper(II) atoms