DEMON: a Local-First Discovery Method for Overlapping Communities

Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. However, many large networks often lack a particular community organization at a global level. In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. We propose here a simple local-first approach to community discovery, able to unveil the modular organization of real complex networks. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. We tested this intuition against the state-of-the-art overlapping and non-overlapping community discovery methods, and found that our new method clearly outperforms the others in the quality of the obtained communities, evaluated by using the extracted communities to predict the metadata about the nodes of several real world networks. We also show how our method is deterministic, fully incremental, and has a limited time complexity, so that it can be used on web-scale real networks.Comment: 9 pages; Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, August 12-16, 201

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Networks of innovation: measuring, modelling and enhancing innovation in surgery

The rate of innovation occurring in surgery is beyond our systemic capacity to quantify, with several methodological and practical challenges. The existing paucity of surgical innovation metrics presents a global healthcare problem especially as surgical innovations become increasingly costlier at a time when healthcare provision is experiencing a radical transformation driven by pressures to reduce costs, an ageing population with ever-increasing healthcare needs and patients with growing expectations. This thesis aims to devise a novel, quantitative, network-based framework that will permit modelling and measuring surgical innovation to add the most value to patient care. It involves the systematic, graphical and analytical assessment of surgical innovation in a way that has never been done before. This is based on successful models previously applied in the industry with advanced analytical techniques derived from social science (network analysis). In doing so, it offers an exciting new perspective and opportunity for understanding how the innovation process originates and evolves in surgery and how it can be measured in terms of value and virality, a priority for the NHS, RCS, Imperial and the wider surgical community. The ability to measure value and rank innovations is expected to play a fundamental role in guiding policy, strategically direct surgical research funding, and uncover innovation barriers and catalysts. This will ensure participation in the forefront of novel surgical technology and lay the scientific foundations for the development of improved healthcare models and services to enhance the quality of healthcare delivered.Open Acces

Influence Analysis for Online Social Networks

Ph.DDOCTOR OF PHILOSOPH

The Role of Influentials in the Diffusion of New Products

This dissertation comprises three separate essays that deal with the role of influentials in the diffusion of new products. Influentials are a small group of consumers who are likely to play an important role in the diffusion of a new product through their propensity to adopt the product early and/or their persuasive influence on others’ new product adoption decisions. The literature labels these consumers as opinion leaders, social hubs, innovators, early adopters, lead users, experts, market mavens, and boundary spanners. This dissertation integrates two perspectives that researchers have mostly studied independently: market-level, which investigates the spread of a new product (e.g., total number of products sold) across markets over time as a function of aggregate-level marketing and social parameters; and individual-level, which considers how to identify influentials and their impact on the adoption behaviors of others. The first essay reviews and integrates the literature on the role of influentials in the diffusion of new products from a marketing management perspective. The study develops a framework using the individual- and market-level research perspectives to highlight five major interrelated areas: the two theoretical bases of why influentials have a high propensity to adopt new products early and why they considerably influence others’ adoption decisions, the issues concerned with how marketers can identify influentials and effectively target them, and how significant individual-level processes lead to significant market-level behavior. The study synthesizes the relevant research findings and suggests future research directions for improving our knowledge of the role of influentials in the diffusion of new products.The second essay explores firms’ decisions regarding the selection of target consumers for seeding—providing free products to enhance the diffusion process. The study examines the profit impact of targeting five groups of potential consumers for seeding under alternative social network structures. The findings suggest that seeding programs generally increase the net present value of profits. Moreover, social hubs—the most connected consumers—offer the best seeding target under most conditions that were examined. However, under certain conditions firms can achieve comparable results through random seeding and save the resources and effort required to identify the social hubs. Finally, the interactions among several variables—the choice of seeding target, consumer social network structure, and variable seeding cost—impact the returns that seeding programs generate and the ‘optimal’ number of giveaways.The third essay explores the adverse impacts of three types of consumer resistance to new products—postponement, rejection, and opposition—on firm profits. The study investigates these effects across five groups of consumers and alternative social network structures. The findings suggest that complex interactions between three groups of parameters—resistance, consumer social network, and diffusion parameters—affect the relationship between resistance and profits. Moreover, opposition reduces firm profits to a degree that is significantly greater than rejection and postponement. Finally, influential resister groups generally have stronger adverse impacts on profits than do randomly designated resisters

Managing brands in the social media environment

The dynamic, ubiquitous, and often real-time interaction enabled by social media significantly changes the landscape for brand management. A deep understanding of this change is critical since it may affect a brand's performance substantially. Literature about social media's impact on brands is evolving, but lacks a systematic identification of key challenges related to managing brands in this new environment. This paper reviews existing research and introduces a framework of social media's impact on brand management. It argues that consumers are becoming pivotal authors of brand stories due to new dynamic networks of consumers and brands formed through social media and the easy sharing of brand experiences in such networks. Firms need to pay attention to such consumer-generated brand stories to ensure a brand's success in the marketplace. The authors identify key research questions related to the phenomenon and the challenges in coordinating consumer- and firm-generated brand stories