Network science

Abstract: It is our great pleasure to welcome you to the 1st ACM International Workshop on Complex Networks in Information & Knowledge Management -- CNIKM'09 We are in a connected age: real-world entities often interconnect with each other through explicit or implicit relationships to form a complex network, such as technological networks, social networks, and information networks They exhibit interesting statistical characteristics like "small-world" and "scale-free" The past decade has witnessed an explosive growth of research on various complex networks How can we analyze, manage and mine information in large-scale complex networks effectively and efficiently? This gives researchers in database, information retrieval and knowledge management great challenges as well as opportunities In line with CIKM's tradition of promoting interdisciplinary research, this workshop aims to bring together researchers across both computer science and the emerging network science to foster discussion and exchange ideas Although these two scientific disciplines speak quite different languages, they certainly can benefit a lot from each other by sharing their concepts, models, techniques, and tools, etc The call for papers attracted 19 submissions from 12 countries (Australia, Brazil, Canada, China, Germany, India, Korea, Netherlands, Singapore, Sweden, Switzerland and United Kingdom) The program committee accepted 9 full papers and 3 short papers that cover a variety of topics, including community detection, information spread, centrality analysis, link prediction, peer-to-peer networks and recommender systems In addition, the program includes a keynote speech by Prof Jure Leskovec of Stanford University on the clustering structure of very large networks We hope that these proceedings will serve as a valuable reference for researchers and practitioners in this area

Abstract: This study combines network science and ethnography to explore how al-Muhajiroun, a banned Islamist network, continued its high-risk activism despite being targeted for disruption by British author...

Abstract: Link prediction in a complex network is a problem of fundamental interest in network science and has attracted increasing attention in recent years. It aims to predict missing (or future) links between two entities in a complex system that are not already connected. Among existing methods, local similarity indices are most popular that take into account the information of common neighbours to estimate the likelihood of existence of a connection between two nodes. In this paper, we propose global and quasi-local extensions of some commonly used local similarity indices. We have performed extensive numerical simulations on publicly available datasets from diverse domains demonstrating that the proposed extensions not only give superior performance, when compared to their respective local indices, but also outperform some of the current, state-of-the-art, local and global link-prediction methods.

Abstract: The rapid growth of online health communities and the increasing availability of relational data from social media provide invaluable opportunities for using network science and big data analytics to better understand how patients and caregivers can benefit from online conversations. Here, we outline a new network-based theory of social medical capital that will open up new avenues for conducting large-scale network studies of online health communities and devising effective policy interventions aimed at improving patients' self-care and health.