Influence Visualization of Scientific Paper through Flow-Based Citation Network Summarization

2015 ◽  
Author(s):  
Yue Su ◽  
Sibai Sun ◽  
Yuan Xuan ◽  
Lei Shi
Keyword(s):  
Citation Network ◽  
Scientific Paper ◽  
Through Flow

Analysis of direct citation, co-citation and bibliographic coupling in scientific topic identification

2020 ◽  
pp. 016555152096277
Author(s):  
Rajmund Kleminski ◽  
Przemysiaw Kazienko ◽  
Tomasz Kajdanowicz
Keyword(s):  
Citation Network ◽  
Scientific Paper ◽  
Added Value ◽  
Bibliographic Coupling ◽  
Science Literature ◽  
Citation Networks ◽  
Research Topics ◽  
Topic Identification ◽  
Direct Citation ◽  
The Impact

In our study, we examine the impact of citation network structures on the ability to discern valuable research topics in Computer Science literature. We use the bibliographic information available in the DBLP database to extract candidate phrases from scientific paper abstracts. Following that, we construct citation networks based on direct citation, co-citation and bibliographic coupling relationships between the papers. The candidate research topics, in the form of keyphrases and n-grammes, are subsequently ranked and filtered by a graph-text ranking algorithm. This selection of the highest ranked potential topics is further evaluated by domain experts and through the Wikipedia knowledge base. The results obtained from these citation networks are complementary, returning valid but non-overlapping output phrases between some pairs of networks. In particular, bibliographic coupling appears to capture more unique information than either direct citation or co-citation. These findings point towards the possible added value in combining bibliographic coupling analysis with other structures. At the same time, combining direct citation and co-citation is put into question. We expect our findings to be utilised in method design for research topic identification.

scholarly journals Fusion of text and graph information for machine learning problems on networks

2021 ◽  
Vol 7 ◽  
pp. e526
Author(s):  
Ilya Makarov ◽  
Mikhail Makarov ◽  
Dmitrii Kiselev
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Citation Network ◽  
Representation Learning ◽  
Scientific Paper ◽  
Learning Problems ◽  
Network Embedding ◽  
Network Properties ◽  
Text Information ◽  
Low Dimensional

Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization. Naturally, some networks have text information associated with them. For instance, in a citation network, each node is a scientific paper associated with its abstract or title; in a social network, all users may be viewed as nodes of a network and posts of each user as textual attributes. In this work, we explore how combining existing methods of text and network embeddings can increase accuracy for downstream tasks and propose modifications to popular architectures to better capture textual information in network embedding and fusion frameworks.

THE DYNACTOME: INVESTIGATING DYNAMIC KINASE NETWORKS IN CELLULAR DECISION-MAKING

2008 ◽  
Vol 31 (4) ◽  
pp. 22
Author(s):  
Jonathan So ◽  
Kelly Elder ◽  
Anna Dai ◽  
Claus Jorgensen ◽  
Rune Linding ◽  
...  
Keyword(s):  
Spectrometric Analysis ◽  
Phosphorylation Sites ◽  
Complex Samples ◽  
Downstream Signaling ◽  
Through Flow ◽  
Colorectal Cancer Cells ◽  
Induced Apoptosis ◽  
Cell Phenotypes ◽  
Kinase Phosphorylation ◽  
Kinase Expression

Networks of kinases play a role in the transmission and integration of signals from the membrane to the nucleus. We aim to elucidate kinase phosphorylation and interaction partners in these networks through the immuno-precipitation and mass spectrometric analysis of a representative set of 100 Flag-tagged kinases stably expressed in human colorectal cancer cells. The goal is to generate a comprehensive set of interactions and dynamic phosphorylation sites which correlate with cell phenotypes such as apoptosis and proliferation. The techniques of mass-spectrometry have allowed for the identification of proteins and their phosphorylation sites in complex samples. Various labeling methods such as iTRAQ has enabled the relative quantification of these sites as afunction of time (White et al. PNAS, 2007). However, kinases usually work in the context of particular signaling stimuli. We aim to characterize the role of these over-expressed kinases in the context of Trail-induced apoptosis. This isparticularly relevant to tumorigenesis in that many cancers are resistant to apoptosis and recombinant Trail therapies are currently undergoing clinical trials. We present assays to correlate the proliferative ability and sensitivity to apoptosis of various stable cell lines with kinase expression levels through flow cytometry. We also present efforts to trace downstream signaling through the monitoring of MAP kinase phosphorylation using a high-throughput bead array.

Analysis of Deposited Salts in the Through-Flow Area of a 210 MW Steam Turbine

2013 ◽  
Vol 55 (10) ◽  
pp. 743-747
Author(s):  
Branko Staniša ◽  
Lidija Ćurković ◽  
Zdravko Schauperl
Keyword(s):  
Steam Turbine ◽  
Flow Area ◽  
Through Flow

A Review of Minimum Quantity Lubrication (MQL) based on Bibliometry

2020 ◽  
Vol 13 ◽  
Author(s):  
Gaurav Gaurav ◽  
Abhay Sharma ◽  
G S Dangayach ◽  
M L Meena
Keyword(s):  
Descriptive Analysis ◽  
Minimum Quantity Lubrication ◽  
Citation Network ◽  
Machining Process ◽  
Cutting Fluid ◽  
Future Research ◽  
Research Directions ◽  
Minimum Quantity ◽  
Research Areas ◽  
Future Research Directions

Background: Minimum quantity lubrication (MQL) is one of the most promising machining techniques that can yield a reduction in consumption of cutting fluid more than 90 % while ensuring the surface quality and tool life. The significance of the MQL in machining makes it imperative to consolidate and analyse the current direction and status of research in MQL. Objective: This study aims to assess global research publication trends and hot topics in the field of MQL among machining process. The bibliometric and descriptive analysis are the tools that the investigation aims to use for the data analysis of related literature collected from Scopus databases. Methods: Various performance parameters are extracted, such as document types and languages of publication, annual scientific production, total documents, total citations, and citations per article. The top 20 of the most relevant and productive sources, authors, affiliations, countries, word cloud, and word dynamics are assessed. The graphical visualisation of the bibliometric data is presented in terms of bibliographic coupling, citation, and co-citation network. Results: The investigation reveals that the International Journal of Machine Tools and Manufacture (2611 citations, 31 hindex) is the most productive journal that publishes on MQL. The most productive institution is the University of Michigan (32 publications), the most cited country is Germany (1879 citations), and the most productive country in MQL is China (124 publications). The study shows that ‘Cryogenic Machining’, ‘Sustainable Machining’, ‘Sustainability’, ‘Nanofluid’ and ‘Titanium alloy’ are the most recent keywords and indications of the hot topics and future research directions in the MQL field. Conclusion: The analysis finds that MQL is progressing in publications and the emerging with issues that are strongly associated with the research. This study is expected to help the researchers to find the most current research areas through the author’s keywords and future research directions in MQL and thereby expand their research interests.

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