Vertex Capacity in a Graph Structure

2013 ◽  
Vol 3 (2) ◽  
pp. 37
Author(s):  
R. B. Gnana Jothi ◽  
R. Ezhil Mary
Keyword(s):  
Graph Structure

Improving VANET Protocols using Graph Structure Approach

2018 ◽  
Vol 6 (6) ◽  
pp. 816-821
Author(s):  
Jagtar Singh ◽  
Sanjay Singla ◽  
Surender Jangra
Keyword(s):  
Graph Structure ◽  
Structure Approach

scholarly journals Bayesian graphical models for modern biological applications

2021 ◽  
Author(s):  
Yang Ni ◽  
Veerabhadran Baladandayuthapani ◽  
Marina Vannucci ◽  
Francesco C. Stingo
Keyword(s):  
Graphical Models ◽  
Cancer Genomics ◽  
Graph Structure ◽  
Biological Processes ◽  
Limited Sample ◽  
Large Networks ◽  
Bayesian Approaches ◽  
Complex Sampling ◽  
Complex Dependence ◽  
Bayesian Graphical Models

AbstractGraphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

scholarly journals Information extraction from digital social trace data with applications to social media and scholarly communication data

2020 ◽  
Vol 54 (1) ◽  
pp. 1-2
Author(s):  
Shubhanshu Mishra
Keyword(s):  
Social Media ◽  
Information Extraction ◽  
Scholarly Communication ◽  
Structured Data ◽  
Graph Structure ◽  
Learning Models ◽  
Social Media Data ◽  
Scholarly Data ◽  
Media Data ◽  
Machine Learning Models

Information extraction (IE) aims at extracting structured data from unstructured or semi-structured data. The thesis starts by identifying social media data and scholarly communication data as a special case of digital social trace data (DSTD). This identification allows us to utilize the graph structure of the data (e.g., user connected to a tweet, author connected to a paper, author connected to authors, etc.) for developing new information extraction tasks. The thesis focuses on information extraction from DSTD, first, using only the text data from tweets and scholarly paper abstracts, and then using the full graph structure of Twitter and scholarly communications datasets. This thesis makes three major contributions. First, new IE tasks based on DSTD representation of the data are introduced. For scholarly communication data, methods are developed to identify article and author level novelty [Mishra and Torvik, 2016] and expertise. Furthermore, interfaces for examining the extracted information are introduced. A social communication temporal graph (SCTG) is introduced for comparing different communication data like tweets tagged with sentiment, tweets about a search query, and Facebook group posts. For social media, new text classification categories are introduced, with the aim of identifying enthusiastic and supportive users, via their tweets. Additionally, the correlation between sentiment classes and Twitter meta-data in public corpora is analyzed, leading to the development of a better model for sentiment classification [Mishra and Diesner, 2018]. Second, methods are introduced for extracting information from social media and scholarly data. For scholarly data, a semi-automatic method is introduced for the construction of a large-scale taxonomy of computer science concepts. The method relies on the Wikipedia category tree. The constructed taxonomy is used for identifying key computer science phrases in scholarly papers, and tracking their evolution over time. Similarly, for social media data, machine learning models based on human-in-the-loop learning [Mishra et al., 2015], semi-supervised learning [Mishra and Diesner, 2016], and multi-task learning [Mishra, 2019] are introduced for identifying sentiment, named entities, part of speech tags, phrase chunks, and super-sense tags. The machine learning models are developed with a focus on leveraging all available data. The multi-task models presented here result in competitive performance against other methods, for most of the tasks, while reducing inference time computational costs. Finally, this thesis has resulted in the creation of multiple open source tools and public data sets (see URL below), which can be utilized by the research community. The thesis aims to act as a bridge between research questions and techniques used in DSTD from different domains. The methods and tools presented here can help advance work in the areas of social media and scholarly data analysis.

scholarly journals Discovering latent node Information by graph attention network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Fei Gao ◽  
Xiaodan Lou ◽  
Jiang Zhang
Keyword(s):  
Supervised Learning ◽  
Link Prediction ◽  
Learning Algorithm ◽  
Network Visualization ◽  
Graph Structure ◽  
High Quality ◽  
Node Centrality ◽  
Influential Nodes ◽  
Node Classification ◽  
Highly Cited

AbstractIn this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information. Compared with node classification based representations, GANR can be used to learn representation for any given graph. GANR is not only capable of learning high quality node representations that achieve a competitive performance on link prediction, network visualization and node classification but it can also extract meaningful attention weights that can be applied in node centrality measuring task. GANR can identify the leading venture capital investors, discover highly cited papers and find the most influential nodes in Susceptible Infected Recovered Model. We conclude that link structures in graphs are not limited on predicting linkage itself, it is capable of revealing latent node information in an unsupervised way once a appropriate learning algorithm, like GANR, is provided.

Graphical structure of extended b-metric spaces: an application to the transverse oscillations of a homogeneous bar

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mudasir Younis ◽  
Deepak Singh ◽  
Ishak Altun ◽  
Varsha Chauhan
Keyword(s):  
Metric Spaces ◽  
Fixed Point Theory ◽  
Directed Graphs ◽  
Point Theory ◽  
Graph Structure ◽  
Open Ball ◽  
Open Problems ◽  
Graphical Structure ◽  
State Of Art ◽  
Transverse Oscillations

Abstract The purpose of this article is to present the notion of graphical extended b-metric spaces, blending the concepts of graph theory and metric fixed point theory. We discuss the structure of an open ball of the new proposed space and elaborate on the newly introduced ideas in a novel way by portraying suitably directed graphs. We also provide some examples in graph structure to show that our results are sharp as compared to the results in the existing state-of-art. Furthermore, an application to the transverse oscillations of a homogeneous bar is entrusted to affirm the applicability of the established results. Additionally, we evoke some open problems for enthusiastic readers for the future aspects of the study.

scholarly journals New Fixed Point Results via a Graph Structure

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1013
Author(s):  
Özlem Acar ◽  
Hassen Aydi ◽  
Manuel De la Sen
Keyword(s):  
Fixed Point ◽  
Metric Space ◽  
Graph Structure ◽  
Contraction Mappings ◽  
Multivalued Contraction

The main aim of this paper is to introduce and study some fixed point results for rational multivalued G-contraction and F-Khan-type multivalued contraction mappings on a metric space with a graph. At the end, we give an illustrative example.

scholarly journals Private Analysis of Graph Structure

2014 ◽  
Vol 39 (3) ◽  
pp. 1-33 ◽  
Author(s):  
Vishesh Karwa ◽  
Sofya Raskhodnikova ◽  
Adam Smith ◽  
Grigory Yaroslavtsev
Keyword(s):  
Graph Structure

Marginal Representation Learning With Graph Structure Self-Adaptation

2018 ◽  
Vol 29 (10) ◽  
pp. 4645-4659 ◽  
Author(s):  
Zheng Zhang ◽  
Ling Shao ◽  
Yong Xu ◽  
Li Liu ◽  
Jian Yang
Keyword(s):  
Representation Learning ◽  
Graph Structure ◽  
Self Adaptation
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