Structure Of Citation Networks Research Articles

Complexity and phase transitions in citation networks: insights from artificial intelligence research.

In this study, we analyze the changes over time in the complexity and structure of words used in article titles and the connections between articles in citation networks, focusing on the topic of artificial intelligence (AI) up to 2020. By measuring unpredictability in word usage and changes in the connections between articles, we gain insights into shifts in research focus and diversity of themes. Our investigation reveals correspondence between fluctuations in word complexity and changes in the structure of citation networks, highlighting links between thematic evolution and network dynamics. This approach not only enhances our understanding of scientific progress but also may help in anticipating emerging fields and fostering innovation, providing a quantitative lens for studying scientific domains beyond AI.

Do the paper’s connections to existing work disclose its citation impact? A study based on graph representation learning

Influential scientific papers tend to be primarily based on combinations of prior works. However, assessing the potential impact of a new scientific paper remains a challenging task. In this article, we introduce an innovative framework to investigate the relationship between the embedding of citation networks and a paper’s future citation counts, based on the graph representation learning approach. First, we employ three Nobel Prize-winning topic papers from the Web of Science as our data source. Through data preprocessing and direct citation network modelling, we train the struc2vec model to obtain embeddings of papers’ citation network structure. Then, we perform visualisation and analysis on two types of networks. One is the direct-citation network, in which we identify four patterns of linkage between newly published papers and existing knowledge, and the other is the co-citation network, where we measure three structural variation indicators of new papers based on existing research findings. Finally, a statistical test is used to examine the predictive potentials of network embeddings. The results demonstrate that the structural features captured by the graph representation learning model can be used to predict a paper’s citation counts and impact. This article innovatively combines cluster analysis, visual analysis and statistical analysis to gain insights into the relationship between the hard-to-explain structural embeddings of newly published papers in a citation network and their future citations.

On the stability of citation networks

Citation networks can reveal important information regarding the development of science and the relationship between different areas of knowledge. Frequently, citation networks are created using articles acquired from a set of relevant keywords or queries. Here, we study the robustness of citation networks communities with regards to the keywords that were used for collecting the respective articles. A perturbation approach is proposed, in which the influence of missing keywords on the community structure of citation networks is quantified. In addition, the relationship between keywords and the community structure of citation networks is studied using networks generated from a simple model. We find that, owing to its highly modular structure, the community structure of citation networks tends to be preserved even when many relevant keywords are left out. Furthermore, the proposed model can reflect the impact of missing keywords on different situations.

The effect of citation behaviour on knowledge diffusion and intellectual structure

Citation behaviour is the source driver of scientific dynamics, and it is essential to understand its effect on knowledge diffusion and intellectual structure. This study explores the effect of citation behaviour on disciplinary knowledge diffusion and intellectual structure by comparing three types of citation behaviour trends, namely the high citation trend, medium citation trend, and low citation trend. The diffusion power, diffusion speed, and diffusion breadth were calculated to quantify knowledge diffusion. The properties of the global and local citation network structure were used to reflect the particular influences of citation behaviour on the scientific intellectual structure. The primary empirical results show that (a) the high citation behaviour trend could improve the knowledge diffusion speed for papers with a short citation history span. Additionally, the medium citation trend has the broadest diffusion breadth whereas the low citation behaviour trend might make the citation counts take off for papers with a long citation history span; (b) the high citation trend has a stronger influence and greater control over the intellectual structure, but this relationship is true only for papers with a short or normal citation history span. These findings could play important roles in scientific research evaluation and impact prediction.

Utilizing citation network structure to predict paper citation counts: A Deep learning approach

With the advancement of science and technology, the number of academic papers published each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the standard for evaluation and decision-making of them, such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very critical. The most common standard for measuring the quality of academic papers is the number of citation counts of them, as this indicator is widely used in the evaluation of scientific publications. It also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers. To improve the effective of citation counts prediction, we try to solve the citation counts prediction problem from the perspective of information cascade prediction and take advantage of deep learning techniques. Thus, we propose an end-to-end deep learning framework (DeepCCP), consisting of graph structure representation and recurrent neural network modules. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and outputs the citation counts of the corresponding paper after a period of time. It only exploits the structure and temporal information of the citation network, and does not require other additional information. According to experiments on two real academic citation datasets, DeepCCP is shown superior to the state-of-the-art methods in terms of the accuracy of citation count prediction.

The local structure of citation networks uncovers expert-selected milestone papers

Recent works aimed to understand how to identify “milestone” scientific papers of great significance from large-scale citation networks. To this end, previous results found that global ranking metrics that take into account the whole network structure (such as Google’s PageRank) outperform local metrics such as the citation count. Here, we show that by leveraging the recursive equation that defines the PageRank algorithm, we can propose a family of local impact metrics. Our results reveal that the obtained PageRank-based local metrics outperform the citation count and other local metrics in identifying the seminal papers. Compared with global metrics, these local metrics can reach similar performance in the identification of seminal papers in shorter computational time, without requiring the whole network topology. Our findings could help to better understand the nature of groundbreaking research from citation network analysis and find practical applications in large-scale data.

Emergence of network effects and predictability in the judicial system

As courts strive to simultaneously remain self-consistent and adapt to new legal challenges, a complex network of of citations between decided cases is established. Using network science methods to analyze the underlying patterns of citations between cases can help us understand the large-scale mechanisms which shape the judicial system. Here, we use the case-to-case citation structure of the Court of Justice of the European Union to examine this question. Using a link-prediction model, we show that over time the complex network of citations evolves in a way which improves our ability to predict new citations. Investigating the factors which enable prediction over time, we find that the content of the case documents plays a decreasing role, whereas both the predictive power and significance of the citation network structure itself show a consistent increase over time. Finally, our analysis enables us to validate existing citations and recommend potential citations for future cases within the court.

Emergence of Network Effects and Predictability in the Judicial System

As courts strive to simultaneously remain self-consistent and adapt to new legal challenges, a complex network of of citations between decided cases is established. Using network science methods to analyze the underlying patterns of citations between cases can help us understand the large-scale mechanisms which shape the judicial system. Here, we use the case-to-case citation structure of the Court of Justice of the European Union to examine this question. Using a link-prediction model, we show that over time the complex network of citations evolves in a way which improves our ability to predict new citations. Investigating the factors which enable prediction over time, we find that the content of the case documents plays a decreasing role, whereas both the predictive power and significance of the citation network structure itself show a consistent increase over time. Finally, our analysis enables us to validate existing citations and recommend potential citations for future cases within the court.

Leveraging citation influences for Modeling scientific documents

This paper studies a link-text algorithm to model scientific documents by citation influences, which is applied to document clustering and influence prediction. Most existing link-text algorithms ignore the different weights of citation influences that cited documents have on the corresponding citing document. In fact, citation influences reveal the latent structure of citation networks which is more accurate to describe the knowledge flow than the original citation structure. In this study, a citation influence is modeled as a weight of linear combination that approximates the text of a document by the content of its citations. Then, we present a novel matrix factorization algorithm, called Citation-Influences-Text Nonnegative Matrix Factorization (CIT-NMF), which incorporates text and citations to obtain better document representations by learning influence weights. In addition, an efficient optimization method is derived to solve the optimization problem. Experimental results on several real datasets show satisfactory improvements over the baseline models.

Non-Invasive Treatment of Adolescent Idiopathic Scoliosis: Systematic Literature Review by Using Citation Network Analysis

There are many nonsurgical treatment options from adolescent idiopathic scoliosis (AIS) to control the progression of the spinal curvature. However, the existing studies are so diverse and diffuse that it is challenging to find potential trends for further studies without a more comprehensive review in place for reference purposes. Thus, the objective of this review article is to conduct a systematic literature review on the noninvasive treatment of AIS with a citation network analysis. The Web of Science (WoS) was searched using 9 reference keywords to collect published articles in English on nonsurgical treatment options for AIS from 1979 to 2019. Each eligible article focused on the nonsurgical options for treating AIS. In addition, the HistCite and the UCINet softwares were referred to for article distribution and citation network analysis. Only non-invasive treatment options of AIS were included. Studies that estimated the amount of spinal curvature, causes of or risk factors for AIS and any invasive treatments, such as surgery, were not focused on. The articles were independently extracted by 3 authors based on the predefined eligibility criteria. The title, authors, year of publication, country or region of publication, reference list, keywords and other bibliographic information from each article was extracted and analyzed purely based on the data with the HistCite and UCINet programs. At last, a total of 146 articles out of 1594 articles were selected for the literature review after the exclusion of 1357 irrelevant articles and 91 inappropriate studies, such as letters and proceedings papers. Of these 146 articles, the majority were empirical studies (74.0%). There was also a lack of meta-analyses in this research field. Based on the results of the citation network analysis, most of the top 10 articles with the highest local citation scores (LCSs) focused on bracing as a treatment. The popularity of bracing as a research topic was also supported by the citation network structure, as brace treatment was in the largest of the five research clusters; this cluster also included posture training through exercise, managing the corrective forces of braces, 3D analysis of braces and quality of life during brace treatment. However, most of these studies focused on rigid braces. A few studies on non-rigid braces and other types of nonsurgical treatment were available in the current literature, which might indicate potential research areas for future studies. To conclude, noninvasive treatment for AIS has been on the academic radar recently; but there are still research gaps, such as the lack of research on semi-rigid and flexible braces or posture training exercises with biofeedback systems. Further studies could explore these research directions.

A hybrid model for the patent citation network structure

Percolation theory on the patent citation network is studied and the percolation threshold points are identified. The results show that there is a significant change of the threshold throughout our dataset years, implying changes in the formation process of the patent citation network. There is a first shift at around 2001, and a very delayed transition point after 2008. Giant component formation in such networks is an indication of the existence of inter-disciplinary patents. In order to explain the changes observed, a hybrid model for creating networks is suggested here. The model is based on a combination of random networks and preferential attachment. It is also compared with results from the well-known configuration model. The hybrid model fits better the data of the patent citation network, rather than a single scale-free or a single Erdős–Rényi network, and explains the increase in preferential attachment in later years. Both the degree distribution and the results of the analysis through percolation theory agree well with real data. This enables the formation of a plausible explanation for the structural changes of the patent citation network’s evolution.

Social Network Analysis of Academic Studies on Gifted People

In this study, 1035 articles on giftedness published between the years of 1956-2016 and indexed in WoS were analyzed in terms of their bibliometric qualities and the social network structure was revealed by means of using CiteSpace 4.0 program in order to present the historical process and the general structure of the field of giftedness and thus help the field to be viewed from above with a bird’s eye. As a result of this research; the distribution of the number of publication and citation of articles on giftedness within the field of education according to years, countries, university/institute labels, and research fields. Moreover, word and keyword network structure, cited journal network structure, co-cited reference network structure and co-cited author citation network structure belonging to these publications were revealed. It is thought that researchers to start a new study will make use of the results of this study so as to get as much information as possible about the field of giftedness.

Stochastic block model reveals maps of citation patterns and their evolution in time

Abstract In this study we map out the large-scale structure of citation networks of science journals and follow their evolution in time by using stochastic block models (SBMs). The SBM fitting procedures are principled methods that can be used to find hierarchical grouping of journals that show similar incoming and outgoing citations patterns. These methods work directly on the citation network without the need to construct auxiliary networks based on similarity of nodes. We fit the SBMs to the networks of journals we have constructed from the data set of around 630 million citations and find a variety of different types of groups, such as communities, bridges, sources, and sinks. In addition we use a recent generalization of SBMs to determine how much a manually curated classification of journals into subfields of science is related to the group structure of the journal network and how this relationship changes in time. The SBM method tries to find a network of blocks that is the best high-level representation of the network of journals, and we illustrate how these block networks (at various levels of resolution) can be used as maps of science.

Detecting trends in academic research from a citation network using network representation learning.

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth.

CiteRivers: Visual Analytics of Citation Patterns.

The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback.

Typifying scientific output: a bibliometric analysis of archaeological publishing across the science/humanities spectrum (2009–2013)

ABSTRACTThis article presents the results of a bibliometric analysis conducted on all original research papers published in six high-ranking archaeological journals between 2009 and 2013, consisting of 926 papers. The purpose is to identify the general features characterizing the output of archaeological publishing within the given time frame and to discuss the results in light of the science/humanities divide of archaeology. It expands previous work, covering not just scientific or humanistic parts of archaeology, but sub-disciplinary niches across the science/humanities-spectrum. Significant differences are identified amongst the journals on an array of parameters, including journal statistics, citation network, thematic distribution, the application of methods and the direction of relevance to other sub-fields. Most significantly, established correlations of academic publishing are for the first time identified in archaeology, regarding the structure of citation networks, the connectedness of high-rank...

New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network

Being able to effectively measure similarity between patents in a complex patent citation network is a crucial task in understanding patent relatedness. In the past, techniques such as text mining and keyword analysis have been applied for patent similarity calculation. The drawback of these approaches is that they depend on word choice and writing style of authors. Most existing graph-based approaches use common neighbor-based measures, which only consider direct adjacency. In this work we propose new similarity measures for patents in a patent citation network using only the patent citation network structure. The proposed similarity measures leverage direct and indirect co-citation links between patents. A challenge is when some patents receive a large number of citations, thus are considered more similar to many other patents in the patent citation network. To overcome this challenge, we propose a normalization technique to account for the case where some pairs are ranked very similar to each other because they both are cited by many other patents. We validate our proposed similarity measures using US class codes for US patents and the well-known Jaccard similarity index. Experiments show that the proposed methods perform well when compared to the Jaccard similarity index.

Universal hierarchical behavior of citation networks

Many of the essential features of the evolution of scientific research are imprinted in the structure of citation networks. Connections in these networks imply information about the transfer of knowledge among papers, or, in other words, edges describe the impact of papers on other publications. This inherent meaning of the edges implies that citation networks can exhibit hierarchical features that are typical of networks based on decision making. In this paper, we investigate the hierarchical structure of citation networks consisting of papers in the same field. We find that the majority of the networks follow a universal trend towards a highly hierarchical state, and the various fields display differences only concerning (i) their phase in life (distance from the ‘birth’ of a field) or (ii) the characteristic time according to which they are approaching the stationary state. We also show by a simple argument that the alterations in the behavior are related to and can be understood by the degree of specialization corresponding to the fields. Our results suggest that during the accumulation of knowledge in a given field, some papers are gradually becoming relatively more influential than most other papers.

A Two-Level Topic Model Towards Knowledge Discovery from Citation Networks

Knowledge discovery from scientific articles has received increasing attention recently since huge repositories are made available by the development of the Internet and digital databases. In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. In the existing topic models, little effort is made to differentiate these two roles. We believe that the topic distributions of these two roles are different and related in a certain way. In this paper, we propose a Bernoulli process topic (BPT) model which considers the corpus at two levels: document level and citation level. In the BPT model, each document has two different representations in the latent topic space associated with its roles. Moreover, the multi-level hierarchical structure of citation network is captured by a generative process involving a Bernoulli process. The distribution parameters of the BPT model are estimated by a variational approximation approach. An efficient computation algorithm is proposed to overcome the difficulty of matrix inverse operation. In addition to conducting the experimental evaluations on the document modeling and document clustering tasks, we also apply the BPT model to well known corpora to discover the latent topics, recommend important citations, detect the trends of various research areas in computer science between 1991 and 1998, and to investigate the interactions among the research areas. The comparisons against state-of-the-art methods demonstrate a very promising performance. The implementations and the data sets are available online .

Topic formation and development: a core-group evolving process

Recent years have witnessed increased interests in topic detection and tracking (TDT). However, existing work mainly focuses on overall trend analysis, and is not developed for understanding the evolving process of topics. To this end, this paper aims to reveal the underlying process and reasons for topic formation and development (TFD). Along this line, based on community partitioning in social networks, a core-group model is proposed to explain the dynamics and to segment topic development. This model is inspired by the cell division mechanism in biology. Furthermore, according to the division phase and interphase in the life cycle of a core group, a topic is separated into four states including birth state, extending state, saturation state and shrinkage state. In this paper, we mainly focus our studies on scientific topic formation and development using the citation network structure among scientific papers. Experimental results on two real-world data sets show that the division of a core group brings on the generation of a new scientific topic. The results also reveal that the progress of an entire scientific topic is closely correlated to the growth of a core group during its interphase. Finally, we demonstrate the effectiveness of the proposed method in several real-life scenarios.