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This article presents a scientometric analysis of 330,729 journal articles indexed in the prestigious Science Citation Index Expanded of the Web of Science database authored by researchers affiliated with institutions from five Southeast Asian countries (Indonesia, Malaysia, Philippines, Thailand, and Vietnam) and published in the first two decades of the 21st century (2001-2020). With a focus on Indonesia, we examined the scientific production of those five nations as well as the citedness of their research publications from both the static and dynamic perspectives and compared various indicators of countries in absolute and relative terms, including the socio-economic aspects of population size, gross domestic product, and research and development expenditures. The key findings are: a) the overall research production of all five countries has been growing, b) Indonesia is the weakest nation in all relative scientometric indicators except the average number of citations per paper, where it is ranked second, c) Malaysia is generally the leading nation, but Vietnam has the most remarkably positive development in both production and citedness, and d) the Philippines is extremely efficient in the mean number of citations per paper as well as per expenditure on research and development.

Účel: Tento příspěvek přináší výsledky případové studie zabývající se historickými kořeny východoevropských počítačových vědců. Design/metodologie/přístup: Studie je založena na analýze citovaných referencí pocházejících ze souboru přibližně 80 000 odborných informatických článků východoevropských výzkumníků publikovaných od roku 1989 do roku 2014. Využitím metody nazvané „referenční spektroskopie podle roku vydání (RPYS)“ pro historické analýzy založené na bibliometrických datech analyzujeme celkem 800 000 referencí citovaných v těchto publikacích. Identifikujeme nejvýznamnější roky zahrnující nejčastěji citovaná díla ( z let 1952, 1965 a 1975) a zaměřujeme se na tyto v oboru natolik vynikající publikační výstupy. Ukazujeme, jak se tyto vlivné publikace citovaly ve východní Evropě a obecně a na jaké vědní obory měly největší dopad. Výsledky: Pozoruhodnou publikací, jež měla podle všeho enormní vliv na východoevropskou počítačovou vědu, je Zadehův článek „Fuzzy sets“ v časopise „Information and Control“ z roku 1965. Demonstrujeme, že informatici z východní Evropy jsou v citačním chování konzervativnější než jejich protějšky ze západní Evropy a mají sklon citovat a starší a zavedenější výzkum. Originalita/hodnota: Jaké jsou historické kořeny vědců pracujících v určitém oboru nebo na konkrétním tématu? Existují konkrétní publikace – přelomová díla – která jsou důležitá pro jejich výzkum? Domníváme se, že tyto otázky zajímají výzkumníky mnoha vědních disciplín. Purpose: The current article presents the results of a case study dealing with the historical roots of Eastern European researchers in computer science. Design/methodology/approach: The study is based on an analysis of cited references stemming from a collection of around 80,000 computer science papers by Eastern European researchers published from 1989 to 2014. By using a method called “reference publication year spectroscopy” (RPYS) for historical analyses based on bibliometric data, we analyze around 800,000 references cited in those papers. We identify the peak years including most frequently cited publications (from 1952, 1965, and 1975) and focus on these outstanding works for the field. We show how these influential papers were cited in Eastern Europe and in general and on which scientific fields they have the most impact. Findings: A noteworthy publication that seems to have a tremendous effect on Eastern European computer science is Zadeh’s “Fuzzy sets” article which appeared in Information and Control in 1965. We demonstrate that computer scientists from Eastern Europe are more conservative in their citation behavior and tend to refer to older and more established research than their counterparts from the West. Originality/value: Which are the historical roots of researchers working in a particular field or on a specific topic? Are there certain publications – landmark papers – which are important for their research? We guess that these are questions bothering researchers in many fields.

V tomto článku testujeme hypotézu, že informatické články kladoucí otázky (tj. s otazníkem na konci názvu) se citují častěji než ty, které tuto vlastnost nemají. Za tímto účelem analyzujeme datovou sadu s téměř dvěma miliony záznamů o odborných článcích v počítačových oborech, jež jsou indexovány v databázi Web of Science, a zaměřujeme svou pozornost na průměrnou citovanost příspěvků v jejích specifických podmnožinách. Hlavním poznatkem je, že průměrná citovanost tzv. „dotazujících se článků“ je o téměř 20 % vyšší než u jiných příspěvků a že tento rozdíl je statisticky významný. In this article, we test the hypothesis that computer science papers asking questions (i.e., those with a question mark at the end of their title) are cited more frequently than those that do not have this property. To this end, we analyze a data set of almost two million records on computer science papers indexed in the Web of Science database and focus our investigation on the mean number of citations per paper of its specific subsets. The main finding is that the average number of citations per paper of the so-called “asking papers” is greater by almost 20% than that of other papers, and that this difference is statistically significant.

V několika posledních letech se metody strojového učení (zvláště ty zabývající se detekcí odlehlých hodnot) v oblasti kyberbezpečnosti opíraly o zjišťování anomálií síťového provozu spočívajících v nových schématech útoků. Avšak všudypřítomnost masivních průběžně generovaných datových toků představuje ohromnou výzvu pro účinná detekční schémata a vyžaduje rychlé paměťově nenáročné online algoritmy schopné se potýkat se změnami konceptu. Volba vlastností hraje důležitou roli při zlepšování detekce odlehlých hodnot identifikací zašuměných dat, která obsahují nerelevantní nebo nadbytečné vlastnosti. Současný výzkum se zaměřuje buď na výběr vlastností bez učitele pro průběžně přicházející data nebo na (offline) detekci odlehlých hodnot. V této práci jsou zformulovány podstatné požadavky na kombinaci obou přístupů a dále jsou porovnány s existujícími řešeními. Obsáhlá rešerše odhalila mezeru ve výběru vlastností bez učitele pro zlepšování již hotových metod detekce odlehlých hodnot v datových tocích. Takže navrhujeme nový algoritmus volby vlastností bez učitele pro detekci odlehlých hodnot v průběžně přicházejících datech označovaný jako UFSSOD, který je schopen takové hodnoty automaticky odhalovat. Navíc umí zjišťovat množství nejlepších vlastností shlukováním jejich vypočítaných hodnot. Následně odvozujeme generický koncept, který ukazuje dva aplikační scénáře UFSSOD ve spojení s již hotovými online algoritmy detekce odlehlých hodnot. Rozsáhlé experimenty ukázaly, že slibný mechanismus volby vlastností pro průběžně přicházející data není v oblasti detekce odlehlých hodnot k dispozici. Nadto UFSSOD coby algoritmus schopný online zpracování vykazuje srovnatelné výsledky jako současná nejlepší offline metoda upravená pro detekci odlehlých hodnot. Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns. However, the ubiquity of massive continuously generated data streams poses an enormous challenge to efficient detection schemes and demands fast, memory-constrained online algorithms that are capable to deal with concept drifts. Feature selection plays an important role when it comes to improve outlier detection in terms of identifying noisy data that contain irrelevant or redundant features. State-of-the-art work either focuses on unsupervised feature selection for data streams or (offline) outlier detection. Substantial requirements to combine both fields are derived and compared with existing approaches. The comprehensive review reveals a research gap in unsupervised feature selection for the improvement of outlier detection methods in data streams. Thus, a novel algorithm for Unsupervised Feature Selection for Streaming Outlier Detection, denoted as UFSSOD, will be proposed, which is able to perform unsupervised feature selection for the purpose of outlier detection on streaming data. Furthermore, it is able to determine the amount of top-performing features by clustering their score values. A generic concept that shows two application scenarios of UFSSOD in conjunction with off-the-shell online outlier detection algorithms has been derived. Extensive experiments have shown that a promising feature selection mechanism for streaming data is not applicable in the field of outlier detection. Moreover, UFSSOD, as an online capable algorithm, yields comparable results to a state-of-the-art offline method trimmed for outlier detection.

Future-oriented networking infrastructures are characterized by highly dynamic Streaming Data (SD) whose volume, speed and number of dimensions increased significantly over the past couple of years, energized by trends such as Software-Defined Networking or Artificial Intelligence. As an essential core component of network security, Intrusion Detection Systems (IDS) help to uncover malicious activity. In particular, consecutively applied alert correlation methods can aid in mining attack patterns based on the alerts generated by IDS. However, most of the existing methods lack the functionality to deal with SD data affected by the phenomenon called concept drift and are mainly designed to operate on the output from signature-based IDS. Although unsupervised Outlier Detection (OD) methods have the ability to detect yet unknown attacks, most of the alert correlation methods cannot handle the outcome of such anomaly-based IDS. In this paper, we introduce a novel framework called Streaming Outlier Analysis and Attack Pattern Recognition, denoted as SOAAPR, which is able to process the output of various online unsupervised OD methods in a streaming fashion to extract information about novel attack patterns. Three different privacy-preserving, fingerprint-like signatures are computed from the clustered set of correlated alerts by SOAAPR, which characterizes and represents the potential attack scenarios with respect to their communication relations, their manifestation in the data's features and their temporal behavior. Beyond the recognition of known attacks, comparing derived signatures, they can be leveraged to find similarities between yet unknown and novel attack patterns. The evaluation, which is split into two parts, takes advantage of attack scenarios from the widely-used and popular CICIDS2017 and CSE‐CIC‐IDS2018 datasets. Firstly, the streaming alert correlation capability is evaluated on CICIDS2017 and compared to a state-of-the-art offline algorithm, called Graph-based Alert Correlation (GAC), which has the potential to deal with the outcome of anomaly-based IDS. Secondly, the three types of signatures are computed from attack scenarios in the datasets and compared to each other. The discussion of results, on the one hand, shows that SOAAPR can compete with GAC in terms of alert correlation capability leveraging four different metrics and outperforms it significantly in terms of processing time by an average factor of 70 in 11 attack scenarios. On the other hand, in most cases, all three types of signatures seem to reliably characterize attack scenarios such that similar ones are grouped together, with up to 99.05% similarity between the FTP and SSH Patator attack.intrusion detection, alert analysis, alert correlation, outlier detection, attack scenario, streaming data, network security

V posledních letech se detekce anomálií v počítačových sítích reálného světa stala stále obtížnější kvůli trvalému nárůstu vysoce objemných, rychlých a dimenzionálních průběžně přicházejících dat, pro která nejsou k dispozici obecně uznané a pravdivé informace o anomalitě. Účinná detekční schémata pro vestavěná síťová zařízení musejí být rychlá a paměťově nenáročná a musejí být schopna se potýkat se změnami konceptu, když se vyskytnou. Pro online detekci anomálií bez učitele byly navrženy různé přístupy, které za těchto podmínek spolehlivě identifikují zlovolnou aktivitu. V tomto příspěvku představujeme nový aplikační rámec nazvaný PCB-iForest, jenž je obecně schopen využít jakoukoliv online metodu pro detekci extrémních hodnot založenou na množinách dat tak, aby fungovala na průběžně přicházejících datech. Pečlivě zformulované požadavky srovnáváme s nejpopulárnějšími stávajícími uznávanými online metodami se zvláštním zřetelem na varianty široce přijímaného algoritmu izolovaného lesa a ukazujeme při tom, že dosud neexistovalo flexibilní a výkonné řešení, které přináší až algoritmus PCB-iForest. Proto do tohoto algoritmu integrujeme dvě varianty – zlepšení izolovaného lesa, jež se nazývá rozšířený izolovaný les, a klasickou variantu izolovaného lesa vybavenou funkcionalitou k ohodnocení vlastností podle jejich přispění k anomalitě datového vzorku. Provádíme rozsáhlé experimenty na 23 multidisciplinárních datových sadách týkajících se bezpečnostní problematiky reálného světa za účelem podrobného srovnání naší implementace s již existujícími metodami. Diskuse našich výsledků zahrnující indikátory AUC, F1 a průměrnou dobu zpracování ukazuje, že PCB-iForest jasně překonává už zavedené konkurenční metody v 61 % případů a dokonce dosahuje ještě slibnějších výsledků co do vyváženosti mezi výpočetními náklady na klasifikaci a její úspěšností. In recent years, detecting anomalies in real-world computer networks has become a more and more challenging task due to the steady increase of high-volume, high-speed and high-dimensional streaming data, for which ground truth information is not available. Efficient detection schemes applied on networked embedded devices need to be fast and memory-constrained and must be capable of dealing with concept drifts when they occur. Different approaches for unsupervised online outlier detection have been designed to deal with these circumstances in order to reliably detect malicious activity. In this paper, we introduce a novel framework called PCB-iForest, which generalized, is able to incorporate any ensemble-based online OD method to function on streaming data. Carefully engineered requirements are compared to the most popular state-of-the-art online methods with an in-depth focus on variants based on the widely accepted isolation forest algorithm, thereby highlighting the lack of a flexible and efficient solution which is satisfied by PCB-iForest. Therefore, we integrate two variants into PCB-iForest—an isolation forest improvement called extended isolation forest and a classic isolation forest variant equipped with the functionality to score features according to their contributions to a sample’s anomalousness. Extensive experiments were performed on 23 different multi-disciplinary and security-related real-world datasets in order to comprehensively evaluate the performance of our implementation compared with off-the-shelf methods. The discussion of results, including AUC, F1 score and averaged execution time metric, shows that PCB-iForest clearly outperformed the state-of-the-art competitors in 61% of cases and even achieved more promising results in terms of the tradeoff between classification and computational costs.

In this article some recent disputes about the usefulness of PageRank-based methods for the task of identifying influential researchers in citation networks are discussed. In particular, it focuses on the performance of these methods in relation to simple citation counts. With the aim of comparing these two classes of ranking methods, we analyze a large citation network of authors based on almost two million computer science papers and apply four PageRank-based and citations-based techniques to rank authors by importance throughout the period 1990–2014 on a yearly basis. We use ACM SIGMOD E. F. Codd Innovations Award and ACM A. M. Turing Award winners in our baseline lists of outstanding scientists and define four relevance weighting schemes with some predictive power for the ranking methods to increase the relevance of researchers winning in the future. We conclude that citations-based rankings perform better for Codd Award winners, but PageRank-based methods do so for Turing Award recipients when using absolute ranks and PageRank-based rankings outperform the citations-based techniques for both Codd and Turing Award laureates when relative ranks are considered. However, the two ranking groups show smaller differences if more weight is assigned to the relevance of future awardees.

Všudypřítomné propojování přístrojů ve všech sférách života je podporováno různými trendy, jako je např. Internet věcí. Slibné technologie, jež mohou být používány pro taková budoucí prostředí, pocházejí z bezdrátových senzorových sítí. Ty zajišťují konektivitu mezi distribuovanými, malými a jednoduchými senzorovými uzly a také senzorovými uzly a základními stanicemi za účelem monitorování fyzických a environmentálních podmínek jako např. vibrací, teploty nebo pohybu. Bezpečnost pohraje stále významnější úlohu v následujících desetiletích, v nichž se útočné strategie stanou více a více sofistikovanými. Současné kryptografické mechanismy budou v blízké budoucnosti čelit velké hrozbě ze strany kvantových počítačů a spolu se systémy pro detekci vniknutí jsou jen sotva aplikovatelné na senzory kvůli přílišným nárokům na zdroje. Proto v této práci navrhujeme odlehčený a zdrojově úsporný bezpečnostní koncept budoucnosti pro senzorické sítě, který ve fázi zpracování využívá pronikavého filtrovací mechanismu. Zvláštní pozornost při hodnocení tohoto konceptu je upřena na nový filtr založený na tzv. magických číslech, jenž zmírňuje určité druhy útoku typu Denial-of-Service (odepření služby) provedené na mikrokontrolérových deskách CC1350 LaunchPad ARM Cortex-M3. Operational direct current grids (ODCG) are one of the most important components of many power plants and substations, for example, hydroelectric power plants. They are designed for power supply of relay protection devices, signalization and automation, and for the power supply of important equipment, emergency lighting, drives of high-voltage switches, etc. The importance of this type of equipment leads to high-reliability demands of the ODCG. Quality of cable insulation of the ODCG is crucial. The research conducted in this field shows that both gradual and sudden short-term insulation resistance fluctuations depend on many factors. Among them are the mode of operation, network load, state of cable channels, and state of switching equipment. The results of five-year observations on insulation resistance of open distribution unit ODU-110/330 kV Dniester HPP-1, an distribution unit of DU-35 kV HPP-2 and a complete distribution unit CDU-330 kV of a pumping station in 2012-2017 are analyzed.

Kvůli efektivní ochraně komunikace v bezdrátových senzorických sítích (WSN), jež bude v blízké budoucnosti čelit mj. hrozbám pocházejícím od kvantových počítačů, je na tomto poli nezbytné prozkoumat použitelnost kvantově rezistentních mechanismů. Účelem tohoto článku je přinést přehled možných schémat použitelných v senzorických uzlech a porovnat energetickou spotřebu zvolených volně dostupných softwarových implementací mikrokontrolérovou deskou Texas Instruments CC1350 LaunchPad ARM® Cortex®-M3 vhodnou pro WSN. For an effective protection of the communication in Wireless Sensor Networks (WSN) facing e.g. threats by quantum computers in the near future, it is necessary to examine the applicability of quantum-resistant mechanisms in this field. It is the aim of this article to survey possible candidate schemes utilizable on sensor nodes and to compare the energy consumption of a selection of freely-available software implementations using a WSN-ready Texas Instruments CC1350 LaunchPad ARM® Cortex®-M3 microcontroller board.

The protection of internetworked systems by cryptographic techniques have crystallized as a fundamental aspect in establishing secure systems. Complementary, detection mechanisms for instance based on Intrusion Detection Systems has established itself as a fundamental part in holistic security eco-systems in the previous years. However, the interpretation of and reaction on detected incidents is still a challenging task. In this paper an incident handling environment with relevant components and exemplary functionality is proposed that involves the processes from the detection of incidents over their analysis to the execution of appropriate reactions. An evaluation of a selection of implemented interacting components using technology such as OpenFlow or Snort generally proofs the concept.

Convexity in a network (graph) has been recently defined as a property of each of its subgraphs to include all shortest paths between the nodes of that subgraph. It can be measured on the scale [0, 1] with 1 being assigned to fully convex networks. The largest convex component of a graph that emerges after the removal of the least number of edges is called a convex skeleton. It is basically a tree of cliques, which has been shown to have many interesting features. In this article the notions of convexity and convex skeletons in the context of scientific collaboration networks are discussed. More specifically, we analyze the co-authorship networks of Slovenian researchers in computer science, physics, sociology, mathematics, and economics and extract convex skeletons from them. We then compare these convex skeletons with the residual graphs (remainders) in terms of collaboration frequency distributions by various parameters such as the publication year and type, co-authors' birth year, status, gender, discipline, etc. We also show the top-ranked scientists by four basic centrality measures as calculated on the original networks and their skeletons and conclude that convex skeletons may help detect influential scholars that are hardly identifiable in the original collaboration network. As their inherent feature, convex skeletons retain the properties of collaboration networks. These include high-level structural properties but also the fact that the same authors are highlighted by centrality measures. Moreover, the most important ties and thus the most important collaborations are retained in the skeletons., Comment: 44 pages, 17 figures, 5 tables

Purpose – The purpose of this paper is to study the development of research in computer science in 15 Eastern European countries following the breaching of the Berlin Wall in 1989. Design/methodology/approach – The authors conducted a bibliometric analysis of 82,121 computer science publications indexed in the Web of Science database and investigated publication, citation, and collaboration patterns of the individual countries. Findings – Poland has been the most productive country, followed by Russia, the Czech Republic, Romania, Hungary, and Slovenia. Publication rates have increased substantially over the period, but this has not been accompanied by a corresponding increase in the quality of the publications. Hungary and Slovenia are the most influential countries in terms of citations per paper. Artificial Intelligence is the most frequently occurring computer science subject category, with Interdisciplinary Applications the category with the greatest impact. USA, Germany, UK, France, and Canada are the most frequently collaborating western nations, and papers published in collaboration with US authors accrue the most citations. Originality/value – This is the first ever bibliometric study of the whole post-communist Eastern European computer science research as indexed in the Web of Science.

In this article we present a bibliometric study of 1.9 million computer science papers published from 1945 to 2014 and indexed in Web of Science. We analyze both the quantity and the impact of these publications according to document types, languages, disciplines, countries, institutions, and publication sources. The most frequent author keywords, cited references, and cited papers as well as the distribution of the number of references and citations per paper and of the age of cited references are also explored. Since conference proceedings play a tremendous role in this scientific field, we investigate the time and place of computer science conferences in terms of the most prolific months and locations. And, last but not least, the production of journal articles and conference papers over the whole time period and the level of collaboration in different computer science disciplines are inspected. One of the main results is the finding that “Artificial Intelligence” is the most productive subfield of computer science, but “Interdisciplinary Applications” has the highest relative impact.

Today's trend of an increasing number of networked embedded devices pervades many areas. Ranging from home automation, industrial or automotive applications with a large number of different protocols, low resources and often high demands on real-time make it difficult to secure the communication of such systems. A concept of an uncoupled MAC which is able to ensure the authenticity and integrity of communication flows between two network parties can be used. This is in particular of advance for outdated legacy components still participating in the network. In this paper a assessment simulation model of the mechanism behind this technology is described. It outlines the probability of detecting an attack depending on the message authentication overhead. The model considers all control variables and performs measurements based on random data traffic. The results of the statistical analysis state that a high attack detection rate can be obtained even with a small communication overhead.

This paper explores a possible approach to a research evaluation, by calculating the renown of authors of scientific papers. The evaluation is based on the citation analysis and its results should be close to a human viewpoint. The PageRank algorithm and its modifications were used for the evaluation of various types of citation networks. Our main research question was whether better evaluation results were based directly on an author network or on a publication network. Other issues concerned, for example, the determination of weights in the author network and the distribution of publication scores among their authors. The citation networks were extracted from the computer science domain in the ISI Web of Science database. The influence of self-citations was also explored. To find the best network for a research evaluation, the outputs of PageRank were compared with lists of prestigious awards in computer science such as the Turing and Codd award, ISI Highly Cited and ACM Fellows. Our experiments proved that the best ranking of authors was obtained by using a publication citation network from which self-citations were eliminated, and by distributing the same proportional parts of the publications’ values to their authors. The ranking can be used as a criterion for the financial support of research teams, for identifying leaders of such teams, etc.

An index is proposed that is based on the h-index and a 3-year publication/citation window. When updated regularly, it shows the current scientific performance of researchers rather than their lifetime achievement as indicated by common scientometric indicators. In this respect, the new rating scheme resembles established sports ratings such as in chess or tennis. By the example of ACM SIGMOD E.F. Codd Innovations Award winners and Priestley Medal recipients, we illustrate how the new rating can be represented by a single number and visualized.

In this paper we show that the dramatic increase in the number of research articles indexed in the Web of Science database impacts the commonly observed distributions of citations within these articles. First, we document that the growing number of physics articles in recent years is due to existing journals publishing more and more papers rather than more new journals coming into being as it happens in computer science. And second, even though the references from the more recent papers generally cover a longer time span, the newer papers are cited more frequently than the older ones if the uneven paper growth is not corrected for. Nevertheless, despite this change in the distribution of citations, the citation behavior of scientists does not seem to have changed., 14 pages, 5 figures, J. Assoc. Inf. Sci. Tec. (2016)

In the past, recursive algorithms, such as PageRank originally conceived for the Web, have been successfully used to rank nodes in the citation networks of papers, authors, or journals. They have proved to determine prestige and not popularity, unlike citation counts. However, bibliographic networks, in contrast to the Web, have some specific features that enable the assigning of different weights to citations, thus adding more information to the process of finding prominence. For example, a citation between two authors may be weighed according to whether and when those two authors collaborated with each other, which is information that can be found in the co-authorship network. In this study, we define a couple of PageRank modifications that weigh citations between authors differently based on the information from the co-authorship graph. In addition, we put emphasis on the time of publications and citations. We test our algorithms on the Web of Science data of computer science journal articles and determine the most prominent computer scientists in the 10-year period of 1996–2005. Besides a correlation analysis, we also compare our rankings to the lists of ACM A. M. Turing Award and ACM SIGMOD E. F. Codd Innovations Award winners and find the new time-aware methods to outperform standard PageRank and its time-unaware weighted variants.

This article describes the results of our analysis of the data from the CiteSeer digital library. First, we examined the data from the point of view of source top-level Internet domains from which the data were collected. Second, we measured country shares in publications indexed by CiteSeer and compared them to those based on mainstream bibliographic data from the Web of Science and Scopus. And third, we concentrated on analyzing publications and their citations aggregated by countries. This way, we generated rankings of the most influential countries in computer science using several non-recursive as well as recursive methods such as citation counts or PageRank. We conclude that even if East Asian countries are underrepresented in CiteSeer, its data may well be used along with other conventional bibliographic databases for comparing the computer science research productivity and performance of countries.

The CiteSeer digital library is a useful source of bibliographic information. It allows for retrieving citations, co-authorships, addresses, and affiliations of authors and publications. In spite of this, it has been relatively rarely used for automated citation analyses. This article describes our findings after extensively mining from the CiteSeer data. We explored citations between authors and determined rankings of influential scientists using various evaluation methods including citation and in-degree counts, HITS, PageRank, and its variations based on both the citation and collaboration graphs. We compare the resulting rankings with lists of computer science award winners and find out that award recipients are almost always ranked high. We conclude that CiteSeer is a valuable, yet not fully appreciated, repository of citation data and is appropriate for testing novel bibliometric methods.

In this paper, we present several modifications of the classical PageRank formula adapted for bibliographic networks. Our versions of PageRank take into account not only the citation but also the co-authorship graph. We verify the viability of our algorithms by applying them to the data from the DBLP digital library and by comparing the resulting ranks of the winners of the ACM E. F. Codd Innovations Award. Rankings based on both the citation and co-authorship information turn out to be “better” than the standard PageRank ranking.

The basic indicators of a researcher's productivity and impact are still the number of publications and their citation counts. These metrics are clear, straightforward, and easy to obtain. When a ranking of scholars is needed, for instance in grant, award, or promotion procedures, their use is the fastest and cheapest way of prioritizing some scientists over others. However, due to their nature, there is a danger of oversimplifying scientific achievements. Therefore, many other indicators have been proposed including the usage of the PageRank algorithm known for the ranking of webpages and its modifications suited to citation networks. Nevertheless, this recursive method is computationally expensive and even if it has the advantage of favouring prestige over popularity, its application should be well justified, particularly when compared to the standard citation counts. In this study, we analyze three large datasets of computer science papers in the categories of artificial intelligence, software engineering, and theory and methods and apply 12 different ranking methods to the citation networks of authors. We compare the resulting rankings with self-compiled lists of outstanding researchers selected as frequent editorial board members of prestigious journals in the field and conclude that there is no evidence of PageRank-based methods outperforming simple citation counts., 28 pages, 5 figures, 6 tables

Modern bibliographic databases provide the basis for scientific research and its evaluation. While their content and structure differ substantially, there exist only informal notions on their reliability. Here we compare the topological consistency of citation networks extracted from six popular bibliographic databases including Web of Science, CiteSeer and arXiv.org. The networks are assessed through a rich set of local and global graph statistics. We first reveal statistically significant inconsistencies between some of the databases with respect to individual statistics. For example, the introduced field bow-tie decomposition of DBLP Computer Science Bibliography substantially differs from the rest due to the coverage of the database, while the citation information within arXiv.org is the most exhaustive. Finally, we compare the databases over multiple graph statistics using the critical difference diagram. The citation topology of DBLP Computer Science Bibliography is the least consistent with the rest, while, not surprisingly, Web of Science is significantly more reliable from the perspective of consistency. This work can serve either as a reference for scholars in bibliometrics and scientometrics or a scientific evaluation guideline for governments and research agencies., 16 pages, 3 figures, 3 tables