Importance Sketching of Influence Dynamics in Billion-scale Networks

The blooming availability of traces for social, biological, and communication networks opens up unprecedented opportunities in analyzing diffusion processes in networks. However, the sheer sizes of the nowadays networks raise serious challenges in computational efficiency and scalability. In this paper, we propose a new hyper-graph sketching framework for inflence dynamics in networks. The central of our sketching framework, called SKIS, is an efficient importance sampling algorithm that returns only non-singular reverse cascades in the network. Comparing to previously developed sketches like RIS and SKIM, our sketch significantly enhances estimation quality while substantially reducing processing time and memory-footprint. Further, we present general strategies of using SKIS to enhance existing algorithms for influence estimation and influence maximization which are motivated by practical applications like viral marketing. Using SKIS, we design high-quality influence oracle for seed sets with average estimation error up to 10x times smaller than those using RIS and 6x times smaller than SKIM. In addition, our influence maximization using SKIS substantially improves the quality of solutions for greedy algorithms. It achieves up to 10x times speed-up and 4x memory reduction for the fastest RIS-based DSSA algorithm, while maintaining the same theoretical guarantees.Comment: 12 pages, to appear in ICDM 2017 as a regular pape

Big Networks: Analysis and Optimal Control

The study of networks has seen a tremendous breed of researches due to the explosive spectrum of practical problems that involve networks as the access point. Those problems widely range from detecting functionally correlated proteins in biology to finding people to give discounts and gain maximum popularity of a product in economics. Thus, understanding and further being able to manipulate/control the development and evolution of the networks become critical tasks for network scientists. Despite the vast research effort putting towards these studies, the present state-of-the-arts largely either lack of high quality solutions or require excessive amount of time in real-world `Big Data\u27 requirement. This research aims at affirmatively boosting the modern algorithmic efficiency to approach practical requirements. That is developing a ground-breaking class of algorithms that provide simultaneously both provably good solution qualities and low time and space complexities. Specifically, I target the important yet challenging problems in the three main areas: Information Diffusion: Analyzing and maximizing the influence in networks and extending results for different variations of the problems. Community Detection: Finding communities from multiple sources of information. Security and Privacy: Assessing organization vulnerability under targeted-cyber attacks via social networks

Hypersparse Neural Network Analysis of Large-Scale Internet Traffic

The Internet is transforming our society, necessitating a quantitative understanding of Internet traffic. Our team collects and curates the largest publicly available Internet traffic data containing 50 billion packets. Utilizing a novel hypersparse neural network analysis of "video" streams of this traffic using 10,000 processors in the MIT SuperCloud reveals a new phenomena: the importance of otherwise unseen leaf nodes and isolated links in Internet traffic. Our neural network approach further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents. The inferred model parameters distinguish different network streams and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies. The hypersparse neural network pipeline is highly adaptable and different network statistics and training models can be incorporated with simple changes to the image filter functions.Comment: 11 pages, 10 figures, 3 tables, 60 citations; to appear in IEEE High Performance Extreme Computing (HPEC) 201

Constituting best practice in management consulting

This paper offers critical reflections on the construction and propagation of ‘best practice’: a concept which has become increasingly important in the business world and in civic life more generally. Focusing upon the activities of the Management Consultancies Association (MCA) we offer an analysis of the awards process instituted to applaud ‘best practice’ in the arena of consulting. Departing from existing academic representations of the advice industry which generally exclude this trade body from the analytical frame we consider the role which the MCA performs in the field of consulting. Situating the MCA’s attempt to constitute best practice within the work of Bruno Latour we argue that this construct depends upon the mobilization of an extended network of allies, advocates and spectators whose interactions have been written-out of academic analysis. The paper concludes by proposing the need for further research designed to explore, both, the heterogeneity and the porosity of the networks that construct, convey and applaud key knowledge products such as ‘best practice’

Sharpening the Cutting Edge: Corporate Action for a Strong, Low-Carbon Economy

Outlines lessons learned from early efforts to create a low-carbon economy, current and emerging best practices, and next steps, including climate change metrics, greenhouse gas reporting, effective climate policy, and long-term investment choices

The Cluster as Market Organization

The many competing schools of thought concerning themselves with industrial clusters have at least one thing in common: they all agree that clusters are real life phenomena characterized by the co-localization of separate economic entities, which are in some sense related, but not joined together by any common ownership or management. So hierarchies they are certainly not. Yet, it is usually taken for granted that clusters, almost regardless of how they are defined, all expatriate the 'swollen middle' of various hybrid 'forms of long-term contracting, reciprocal trading, regulation, franchising and the like' residing somewhere between hierarchies and markets. This fundamental (but usually implicit) assumption would, perhaps, be justified if markets could be reduced to events of exchange of property rights, between large numbers of price-taking anonymous buyers and sellers supplied with perfect information as they are commonly conceived in mainstream economics. One of the original attractions of Neoclassical price theory was precisely that it promised a way of analysing the economy in general and market exchange in particular independently of specific institutional settings. However, introducing transaction costs as more than fees paid to intermediaries leads inevitably to comparative institutional analysis and, not to be forgotten, to the perception of markets as institutions with specific characteristics of their own. Some sets of characteristics are so common that they represent a specific market organization or market form. The cluster is one such specific market organization that is structured along territorial lines because this enables the building of a set of institutions that are helpful in conducting certain kinds of economic activities. Supported by empirical illustrations the paper argues that clusters are markets where commodities, services and knowledge are traded in a notably efficient way among the insiders without restricting their abilities to build pipelines and to interact with suppliers and customers residing elsewhere. The institutions characterizing this market form help creating an environment among insiders that reduces the barriers to acquiring and utilising knowledge produced or used locally.Clusters, organisation, knowledge transfer, transaction costs

China´s impact on the global economy: from China price to China standard

China´s entry into the global economy is universally accepted as a defining feature of this new century. Much debate has focused on the impact its economy is having on world market prices, both as producer and consumer. This ´China Price´ effect puts tremendous pressure on Western firms. But China is not just competing on price. Supported by new regulatory institutions, it increasingly influences market rules and technology standards as well. Such Chinese efforts pose a direct challenge to Western competitiveness. While Western firms must adapt to the ´China Price´, countering the ´China Standard´ will require coordination with governments to formulate a countervailing regulatory agenda.

Innovation in Automative Telematics Services: Characteristics of the Field and Management Priciples

The growing role of innovation in the strategy of car manufacturers leads them to relentlessly look for new sources of differentiation. In this way Telematics, a suite of technologies centered on communications systems within cars, is expected to bolster the car industry by offering a new stream of revenues. This articles focuses on the impact of this technology on design organization. In the first part, we demonstrate that Telematics is a radical innovation for automotive industry. Therefore traditional design models, such as heavyweight project management, are unsuitable. Next, the paper studies the organization adopted by a european car manufacturer in the light of recent research on the management of innovation.Management de projet;Services;Télématique automobile;Développement de nouveaux produits;Gestion de l'innovation