This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks. He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion chapters summarizing results conclude the book's two main sections. Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.

We welcome you to the proceedings of the 4th International Workshop on Self-Organizing Systems (IWSOS 2009) hosted at ETH, Zurich, Switzerland. IWSOS provides an annual forum to present and discuss recent research in self-organization focused on networks and networked systems. Research in se- organizingnetworkedsystemshasadvancedinrecentyears,buttheinvestigation of its potentials and limits still leaves challenging and appealing open research issues for this and subsequent IWSOS workshops. Complexandheterogeneousnetworksmakeself-organizationhighlydesirable. Bene?ts envisioned by self-organization are the inherent robustness and ada- ability to new dynamic tra?c, topology changes, and scaling of networks. In - dition to an increasingly complex Future Internet, a number of domain-speci?c subnetworks bene?t from advances in self-organization, including wireless mesh networks, wireless sensor networks, and mobile ad-hoc networks, e.g., vehi- lar ad-hoc networks. Self-organization in networked systems is often inspired by other domains, such as nature (evolution theory, swarm intelligence), sociology (human cooperation), and economics (game theory). Aspects of controllability, engineering,testing,andmonitoringofself-organizingnetworksremainchalle- ing and are of particular interest to IWSOS. This year, we received 34 full paper and 27 short paper submissions. The highquality ofthe submissionsallowedus toprovideastrongtechnicalprogram.

This volume summarizes the author’s work on social information seeking (SIS), and at the same time serves as an introduction to the topic. Sometimes also referred to as social search or social information retrieval, this is a relatively new area of study concerned with the seeking and acquiring of information from social spaces on the Internet. It involves studying situations, motivations, and methods involved in seeking and sharing of information in participatory online social sites, such as Yahoo! Answers, WikiAnswers, and Twitter, as well as building systems for supporting such activities. The first part of the book introduces various foundational concepts, including information seeking, social media, and social networking. As such it provides the necessary basis to then discuss how those aspects could intertwine in different ways to create methods, tools, and opportunities for supporting and leveraging SIS. Next, Part II discusses the social dimension and primarily examines the online question-answering activity. Part III then emphasizes the collaborative aspect of information seeking, and examines what happens when social and collaborative dimensions are considered together. Lastly, Part IV provides a synthesis by consolidating methods, systems, and evaluation techniques related to social and collaborative information seeking. The book is completed by a list of challenges and opportunities for both theoretical and practical SIS work. The book is intended mainly for researchers and graduate students looking for an introduction to this new field, as well as developers and system designers interested in building interactive information retrieval systems or social/community-driven interfaces.

This book focuses on the problem of moving in a cluttered environment with pedestrians and vehicles. A framework based on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data.

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicine