Star X. Zhao

h-Degree as a basic measure in weighted networks

We introduce the h-degree of a node as a basic indicator for weighted networks. The h-degree (dh) of a node is the number dh if this node has at least dh links with other nodes and the strength of each of these links is greater than or equal to dh. Based on the notion of h-degree other notions are developed such as h-centrality and h-centralization, leading to a new set of indicators characterizing nodes in a network.

A study of the “heartbeat spectra” for “sleeping beauties”

We first introduced interesting definitions of “heartbeat” and “heartbeat spectrum” for “sleeping beauties”, based on van Raans variables. Then, we investigated 58,963 papers of Nobel laureates during 1900–2000 and found 758 sleeping beauties. By proposing and using Gs index, an adjustment of Gini coefficient, to measure the inequality of “heartbeat spectrum”, we observed that publications which possess “late heartbeats” (most citations were received in the second half of sleeping period) have higher awakening probability than those have “early heartbeats” (most citations were received in the first half of sleeping period). The awakening probability appears the highest if an articles Gs index exists in the interval [0.2, 0.6).

Exploring the directed h-degree in directed weighted networks

Most networks in information science appear as weighted networks, while many of them (e.g. author citation networks, web link networks and knowledge flow networks) are directed networks. Based on the definition of the h-degree, the directed h-degree is introduced for measuring both weighted networks and directed networks. After analyzing the properties and derived measures of the directed h-degree an actual application of LIS journals citation network is worked out.

Power‐law link strength distribution in paper cocitation networks

A network is constructed by nodes and links, thus the node degree and the link strength appear as underlying quantities in network analysis. While the power‐law distribution of node degrees is verified as a basic feature of numerous real networks, we investigate whether the link strengths follow the power‐law distribution in weighted networks. After testing 12 different paper cocitation networks with 2 methods, fitting in double‐log scales and the Kolmogorov‐Smirnov test (K‐S test), we observe that, in most cases, the link strengths also follow the approximate power‐law distribution. The results suggest that the power‐law type distribution could emerge not only in nodes and informational entities, but also in links and informational connections.

Abstracting the core subnet of weighted networks based on link strengths

Most measures of networks are based on the nodes, although links are also elementary units in networks and represent interesting social or physical connections. In this work we suggest an option for exploring networks, called the h‐strength, with explicit focus on links and their strengths. The h‐strength and its extensions can naturally simplify a complex network to a small and concise subnetwork (h‐subnet) but retains the most important links with its core structure. Its applications in 2 typical information networks, the paper cocitation network of a topic (the h‐index) and 5 scientific collaboration networks in the field of “water resources,” suggest that h‐strength and its extensions could be a useful choice for abstracting, simplifying, and visualizing a complex network. Moreover, we observe that the 2 informetric models, the Glänzel‐Schubert model and the Hirsch model, roughly hold in the context of the h‐strength for the collaboration networks.

A general conceptual framework for characterizing the ego in a network

In this contribution we consider one particular node in a network, referred to as the ego. We combine Zipf lists and ego measures to put forward a conceptual framework for characterizing this particular node. In this framework we unify different forms of h-indices, in particular the h-degree, introduced in the literature. Similarly, different forms of the g-index, the a-index and the R-index are unified. We focus on the pure mathematical and logical concepts, referring to the existing literature for practical examples.

Measuring book impact based on the multi-granularity online review mining

As with articles and journals, the customary methods for measuring books’ academic impact mainly involve citations, which is easy but limited to interrogating traditional citation databases and scholarly book reviews. Researchers have attempted to use other metrics, such as Google Books, libcitation, and publisher prestige. However, these approaches lack content-level information and cannot determine the citation intentions of users. Meanwhile, the abundant online review resources concerning academic books can be used to mine deeper information and content utilizing altmetric perspectives. In this study, we measure the impacts of academic books by multi-granularity mining online reviews, and we identify factors that affect a book’s impact. First, online reviews of a sample of academic books on Amazon.cn are crawled and processed. Then, multi-granularity review mining is conducted to identify review sentiment polarities and aspects’ sentiment values. Lastly, the numbers of positive reviews and negative reviews, aspect sentiment values, star values, and information regarding helpfulness are integrated via the entropy method, and lead to the calculation of the final book impact scores. The results of a correlation analysis of book impact scores obtained via our method versus traditional book citations show that, although there are substantial differences between subject areas, online book reviews tend to reflect the academic impact. Thus, we infer that online reviews represent a promising source for mining book impact within the altmetric perspective and at the multi-granularity content level. Moreover, our proposed method might also be a means by which to measure other books besides academic publications.

Distributive h-indices for measuring multilevel impact

For measuring multilevel impact, we introduce the distributive h-indices, which balance two important components (breadth and strength) of multilevel impact at various citing levels. After exploring the theoretical properties of these indices, we studied two cases: 57 library and information science (LIS) journals and social science research in 38 European countries/territories. Results reveal that there are approximate power-law relations between distributive h-indices and some underlying citation indicators, such as total citations, total citing entities, and the h-index. Distributive h-indices provide comprehensive measures for multilevel impact, and lead to a potential tool for citation analysis, particularly at aggregative levels.

Do funded papers attract more usage

Research funding has been seen as one of the most important resource in the reward system of science. And usage of publications denotes an interesting perspective of user behavior in scientific communication. This study aims to address the relationship between funding and Usage Count, which is a new metrics item established on the platform of Web of Science. Full records of 300,010 articles published in 2013 were downloaded in October 2015, and divided into six disciplines, including information science library science, education educational research, economics, computer science, materials science, and chemistry. Seven indicators were proposed to measure the impact, including Funding rate, Citation per paper, Usage rate, Usage per paper, Citation difference, Usage difference, and Conversion rate. It concluded funding has impact on usage and citation, and funded papers attract more usage, but varying in different disciplines. Usage Count can be used in the extension of citation metrics but with limits. This study originally engages with usage metrics and detected that there is positive correlation between usage and funding.

Characterizing the scholar h-index via full-text citation analysis

This study proposes a method to characterize the scholar h-index by full-text citation analysis. The method combines the citation context analysis, graph mining, and supervised topic modeling to modify the oversimplified process of citation count, and provides more sophisticated assumptions for the scholar h-index in two aspects: the context of citation and the supervised topic-related measure.