The standard and fractional projections are extended from binary two-mode networks to weighted two-mode networks. Some interesting properties of the extended projections are proved.
In this paper, we present the outer product decomposition of a product of compatible linked networks. It provides a foundation for the fractional approach in network analysis. We discuss the standard ...and Newman’s normalization of networks. We propose some alternatives for fractional bibliographic coupling measures.
This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes ...generating these networks and how networks have evolved. Reviews: "this book is easy to read and entertaining, and much can be learned from it. Even if you know just about everything about large-scale and temporal networks, the book is a worthwhile read; you will learn a lot about SNA literature, patents, the US Supreme Court, and European soccer." (Social Networks) "a clear and accessible textbook, balancing symbolic maths, code, and visual explanations. The authors' enthusiasm for the subject matter makes it enjoyable to read" (JASSS)
Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 yearsThis book offers an integrated treatment of network clustering and ...blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling.Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more.Offers a clear and insightful look at the state of the art in network clustering and blockmodelingProvides an excellent mix of mathematical rigor and practical application in a comprehensive mannerPresents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arraysFeatures numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectivelyWritten by leading contributors in the field of spatial networks analysisAdvances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.
This paper presents the analysis of journals publishing articles on social network analysis (SNA). The dataset consists of articles from the Web of Science database obtained by searching for “social ...network*”, works intensively cited, written by the most prominent authors, and published in the main SNA journals up to July 2018. There were 8943 journals in 70,792 articles with complete descriptions. Using a two-mode network linking publications with journals and a one-mode network of citations between articles, we constructed and analysed the networks of citations and bibliographic coupling among journals. Based on the analysis of these networks, we identify the most prominent journals publishing SNA and reveal their relationships to each other. Special attention is given to the position of journal
Social Networks
among other journals in the field. We trace the development of some relationships through time and look at their distributions for selected journals. The results show that the field is growing, which can be seen by the annual rise of the number of journals publishing papers in SNA, and the average number of papers on SNA per journal (almost 3 in recent years). The journals which are currently present in the field belong to social and natural sciences. The social sciences group is represented mainly by journals from sociology and management. Other journals mainly come from the fields of physics, computer science, or are general scientific journals. While journals from social and computer sciences are connected with journals from the same fields, physics journals
Physica A
and
Physical Review E
have developed their own niche. SNA’s main outlet
Social Networks
takes a very coherent and important position. It had explicit primacy up to the 2000s; in recent years its relative input has declined significantly due to the large number of papers published in other journals in the field.
This paper presents a study of authors writing articles in the field of SNA and groups them by means of bibliographic network analysis. The dataset consists of works from the Web of Science database ...obtained by searching for “social network*”, works highly cited in the field, works published in the flagship SNA journals, and written by the most prolific authors (70,000+ publications and 93,000+ authors), up to and including 2018. Using a two-mode network linking publications with authors, we constructed and analysed different types of collaboration networks among authors. We used the temporal quantities approach to trace the development of these networks through time. The results show that most articles are written by 2 or 3 authors. The number of single authored papers has dropped significantly since the 1980s—from 70% to about 10%. The analysis of three types of co-authorship networks allowed us to extract the groups of authors with the largest number of co-authored works and the highest collaborative input, and to calculate the indices of collaborativeness. We looked at the temporal properties of the most popular nodes. We faced the problem of “multiple personalities” of mostly Chinese and Korean authors, which could be overcome with the adoption of standardized author IDs by publishers and bibliographic databases.
This paper presents the results of the analysis of keywords used in Social Network Analysis (SNA) articles included in the WoS database and main SNA journals, from 1970 to 2018. 32,409 keywords were ...obtained from 70,792 works with complete descriptions. We provide a list of the most used keywords and show subgroups of keywords which are connected to each other. To go deeper, we place the keywords into the contexts of selected groups of authors and journals. We use temporal analysis to get an insight into some keyword usage. The distributions of the number of keyword types and tokens over time show fast growth starting from 2010s, which is the result of the growth in the number of articles on SNA topics and applications of SNA in various scientific fields. Even though the most frequently used keywords are trivial or general, the approaches used for the normalization of network link weights allow us to extract keywords representing substantive topics and methodological issues in SNA.
Different research traditions have developed over time to study the quantitative aspects of information and knowledge production, such as
scientometrics
,
bibliometrics
,
librametrics
,
informetrics
...,
cybermetrics
,
webometrics
, or
altmetrics
. These information metrics, or
iMetrics
, as they were labeled by Milojević and Leydesdorff in Scientometrics 95(1):141–157, 2013, are unified by the usage of quantitative data analysis, applying various statistical methods and techniques and are often used to supplement and complement each other. Representing different research traditions, they jointly form a common research field, a “discipline with many names”. In this article, we look at the development of iMetrics field and its evolution over time using bibliometric network analysis and identify its common basis, formed by the most important publications, journals, scholars and topics. The dataset consists of articles from the Web of Science database (26,414 records with complete descriptions). Analyzing the citation network, we evaluate the field’s growth and identify the most cited works. Using the Search path count (SPC) approach, we extract the Main path, Key routes paths, and Link islands in the citation network. The results show that in the last forty years the number of published papers increased, and it doubles every 8 years; the number of single author papers dropped from 50 to 10 %, and the number of papers authored by 3 or more authors is increasing. We make the conclusions about the field’s development and its current state. We also present the main authors, journals and keywords from the field, which form its common basis.
The structure of a large network (graph) can often be revealed by partitioning it into smaller and possibly more dense sub-networks that are easier to handle. One of such decompositions is based on “
...k
-cores”, proposed in 1983 by Seidman. Together with connectivity components, cores are one among few concepts that provide efficient decompositions of large graphs and networks. In this paper we propose an efficient algorithm for determining the cores decomposition of a given network with complexity
, where
m
is the number of lines (edges or arcs). In the second part of the paper the classical concept of
k
-core is generalized in a way that uses a vertex property function instead of degree of a vertex. For local monotone vertex property functions the corresponding generalized cores can be determined in
time, where
n
is the number of vertices and Δ is the maximum degree. Finally the proposed algorithms are illustrated by the analysis of a collaboration network in the field of computational geometry.
Clustering of modal-valued symbolic data Kejžar, Nataša; Korenjak-Černe, Simona; Batagelj, Vladimir
Advances in data analysis and classification,
06/2021, Letnik:
15, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Symbolic data analysis is based on special descriptions of data known as symbolic objects (SOs). Such descriptions preserve more detailed information about units and their clusters than the usual ...representations with mean values. A special type of SO is a representation with frequency or probability distributions (modal values). This representation enables us to simultaneously consider variables of all measurement types during the clustering process. In this paper, we present the theoretical basis for compatible leaders and agglomerative clustering methods with alternative dissimilarities for modal-valued SOs. The leaders method efficiently solves clustering problems with large numbers of units, while the agglomerative method can be applied either alone to a small data set, or to leaders, obtained from the compatible leaders clustering method. We focus on (a) the inclusion of weights that enables clustering representatives to retain the same structure as if clustering only first order units and (b) the selection of relative dissimilarities that produce more interpretable, i.e., meaningful optimal clustering representatives. The usefulness of the proposed methods with adaptations was assessed and substantiated by carefully constructed simulation settings and demonstrated on three different real-world data sets gaining in interpretability from the use of weights (population pyramids and ESS data) or relative dissimilarity (US patents data).
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