Investigating the Impact of the Blogsphere: Using PageRank to Determine the Distribution of Attention

Much has been written in recent years about the blogosphere and its impact on political, educational and scientific debates. Lately the issue has received significant attention from the industry. As the blogosphere continues to grow, even doubling its size every six months, this paper investigates its apparent impact on the overall Web itself. We use the popular Google PageRank algorithm which employs a model of Web used to measure the distribution of user attention across sites in the blogosphere. The paper is based on an analysis of the PageRank distribution for 8.8 million blogs in 2005 and 2006. This paper addresses the following key questions: How is PageRank distributed across the blogosphere? Does it indicate the existence of measurable, visible effects of blogs on the overall mediasphere? Can we compare the distribution of attention to blogs as characterised by the PageRank with the situation for other forms of Web content? Has there been a growth in the impact of the blogosphere on the Web over the two years analysed here? Finally, it will also be necessary to examine the limitations of a PageRank-centred approach

Will This Paper Increase Your h-index? Scientific Impact Prediction

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."Comment: Proc. of the 8th ACM International Conference on Web Search and Data Mining (WSDM'15

Ranking in evolving complex networks

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google’s PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes

Mapping networks of influence: tracking Twitter conversations through time and space

The increasing use of social media around global news events, such as the London Olympics in 2012, raises questions for international broadcasters about how to engage with users via social media in order to best achieve their individual missions. Twitter is a highly diverse social network whose conversations are multi-directional involving individual users, political and cultural actors, athletes and a range of media professionals. In so doing, users form networks of influence via their interactions affecting the ways that information is shared about specific global events. This article attempts to understand how networks of influence are formed among Twitter users, and the relative influence of global news media organisations and information providers in the Twittersphere during such global news events. We build an analysis around a set of tweets collected during the 2012 London Olympics. To understand how different users influence the conversations across Twitter, we compare three types of accounts: those belonging to a number of well-known athletes, those belonging to some well-known commentators employed by the BBC, and a number of corporate accounts belonging to the BBC World Service and the official London Twitter account. We look at the data from two perspectives. First, to understand the structure of the social groupings formed among Twitter users, we use a network analysis to model social groupings in the Twittersphere across time and space. Second, to assess the influence of individual tweets, we investigate the ageing factor of tweets, which measures how long users continue to interact with a particular tweet after it is originally posted. We consider what the profile of particular tweets from corporate and athletes’ accounts can tell us about how networks of influence are forged and maintained. We use these analyses to answer the questions: How do different types of accounts help shape the social networks? and, What determines the level and type of influence of a particular account

Quantifying Success in Science: An Overview

Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions