The Journal of Graph Theory is a high-calibre graphs and combinatorics journal publishing rigorous research on how these areas interact with other mathematical sciences. Our editorial team of influential graph theorists welcome submissions on a range of graph theory topics, such as structural results about graphs, graph algorithms with theoretical emphasis, and discrete optimization on graphs.
(Pdf) Recent Advances in Graph Theory and Its Applications
In. mathematics, graph theory is one of the important fields used in structural. models. This structural structure of different objects or technologies leads to. new developments and changes in ...
Connected Papers
Get a visual overview of a new academic field. Enter a typical paper and we'll build you a graph of similar papers in the field. Explore and build more graphs for interesting papers that you find - soon you'll have a real, visual understanding of the trends, popular works and dynamics of the field you're interested in.
Graph neural networks: A review of methods and applications
In Section 7, we revisit research works over theoretical and empirical analyses of GNNs. ... Find graph structure. The paper focuses on applications on the academic knowledge graph and the recommendation system. In the academic knowledge graph, the graph structure is explicit. In recommendation systems, users, items and reviews can be regarded ...
Knowledge Graphs: A Practical Review of the Research Landscape
Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be simply defined as a directed, labeled, multi-relational graph with some form of semantics. In part, this has been fueled by increased publication of structured datasets on the Web, and well-publicized successes of large-scale ...
A review of graph neural networks: concepts, architectures, techniques
Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks. Models that can learn from such inputs are essential for working with graph data effectively. This paper identifies nodes ...
Knowledge graphs: Introduction, history, and perspectives
INTRODUCTION. The term knowledge graph (KG) has gained several different meanings across a range of usage scenarios. This paper focuses on the use of KGs in the context of two important current trends: the desire and need to harness the large and diverse data that are now available and the advent of new machine learning capabilities for extracting meaning from unstructured text and images.
Explainable artificial intelligence through graph theory by
Some research 29,30 already addressed the raffination of the so-called 'hairball graph' which is overly dominated by unimportant edges. We prefer to use a custom raffination at this step ...
Graph neural networks: A review of methods and applications
images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) ... Lee et al. (2018a) provide a review over graph attention models. The paper proposed by Yang et al. (2020) focuses on heterogeneous graph representation learning, where nodes or edges are of multiple types. Huang et al. (2020) review ...
[1901.00596] A Comprehensive Survey on Graph Neural Networks
Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph ...
[2003.02320] Knowledge Graphs
In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We ...
Graph convolutional networks: a comprehensive review
One main research direction is to define graph convolutions from the spectral perspective, and thus, graph signal processing, such as graph filtering and graph wavelets, has attracted lots of research interests. ... One application on meshes which we consider in this paper is the shape correspondence, i.e., to find correspondences between ...
PDF Research Topics in Graph Theory and Its Applications
f this graph is not F-free, then do this step again.Step 2 Generate a random number. between 1 and 10, and repeat the next step r times.Step 3 Add a vertex v to G and rand. mly generate edges be-tween v and the vertices of G. If the resulting graph is not F-free, then remove the edges incident to v and generate th.
Graph Neural Networks: A bibliometrics overview
Recently, graph neural networks (GNNs) have become a hot topic in machine learning community. This paper presents a Scopus-based bibliometric overview of the GNNs' research since 2004 when GNN papers were first published. The study aims to evaluate GNN research trends, both quantitatively and qualitatively.
Graph Theory and Its Applications in Computing
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. ... On graph pairs, we are able to ...
(PDF) Introduction to Graph Theory
This paper is available from the Center for Research in Computing Technology, Division of Applied Sciences, Harvard University as Technical Report TR-19-94. 1 Introduction In a graph-bisection ...
Inventions
Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm's execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis.
Graph Neural Networks: A Review of Methods and Applications
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the ...
New tool to visualize related articles
A new feature on arXiv.org helps readers explore related academic papers directly from article abstract pages. Developed by Connected Papers and now released as an arXivLabs collaboration, the tool links to interactive visualizations of similar articles. Connected Papers graphs can help readers explore a visual overview of a new academic field, create a bibliography, discover the most relevant ...
An Effective Guide to Explain Graphs in Thesis and Research Paper
By Dr. Sowndarya Somasundaram. May 25, 2023. 7622. When explaining graphs in a thesis and research paper, it is essential to provide a clear and concise interpretation of the data represented in the graph. In this article, iLovePhD presented you with an effective guide to explain graphs in the thesis and research paper.
Announcing Connected Papers
To create each graph, we analyze an order of ~50,000 papers and select the few dozen with the strongest connections to the origin paper (more on that below).
Advances in Human Event Modeling: From Graph Neural Networks to
A systematic overview of deep learning technologies for forecasting and interpreting human events, with a primary focus on political events, and investigates recent achievements in graph neural networks, owing to the prevalence of relational data and the efficacy of graph learning models. Human events such as hospital visits, protests, and epidemic outbreaks directly affect individuals ...
In progress (1 December 2024)
Research Papers; Receive an update when the latest issues in this journal are published. Sign in to set up alerts. Research Papers. select article Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response.
Research on an Assessment Method of Photovoltaic Potential on
By leveraging the data structure advantages of the knowledge graph, this research facilitates the rapid querying of PV potential assessment results for various scales, from Suzhou City down to Gusu District, Wumenqiao Street, or any other location.
[2002.00388] A Survey on Knowledge Graphs: Representation, Acquisition
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2 ...
Graph RAG
Graph databases (DB) such as Neo4j and Arango DB are the straightforward choice. However, extending your tech stack by another db type and learning a new query language (e.g. Cypher/Gremlin) can be time-consuming. From my high-level research, there are also no great serverless options available.
[2102.10014] Social Network Analysis: From Graph Theory to Applications
Social network analysis is the process of investigating social structures through the use of networks and graph theory. It combines a variety of techniques for analyzing the structure of social networks as well as theories that aim at explaining the underlying dynamics and patterns observed in these structures. It is an inherently interdisciplinary field which originally emerged from the ...
[2408.11180] Any Graph is a Mapper Graph
Title: Any Graph is a Mapper Graph Authors: Enrique G Alvarado , Robin Belton , Kang-Ju Lee , Sourabh Palande , Sarah Percival , Emilie Purvine , Sarah Tymochko View a PDF of the paper titled Any Graph is a Mapper Graph, by Enrique G Alvarado and 6 other authors
COMMENTS
The Journal of Graph Theory is a high-calibre graphs and combinatorics journal publishing rigorous research on how these areas interact with other mathematical sciences. Our editorial team of influential graph theorists welcome submissions on a range of graph theory topics, such as structural results about graphs, graph algorithms with theoretical emphasis, and discrete optimization on graphs.
In. mathematics, graph theory is one of the important fields used in structural. models. This structural structure of different objects or technologies leads to. new developments and changes in ...
Get a visual overview of a new academic field. Enter a typical paper and we'll build you a graph of similar papers in the field. Explore and build more graphs for interesting papers that you find - soon you'll have a real, visual understanding of the trends, popular works and dynamics of the field you're interested in.
In Section 7, we revisit research works over theoretical and empirical analyses of GNNs. ... Find graph structure. The paper focuses on applications on the academic knowledge graph and the recommendation system. In the academic knowledge graph, the graph structure is explicit. In recommendation systems, users, items and reviews can be regarded ...
Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be simply defined as a directed, labeled, multi-relational graph with some form of semantics. In part, this has been fueled by increased publication of structured datasets on the Web, and well-publicized successes of large-scale ...
Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks. Models that can learn from such inputs are essential for working with graph data effectively. This paper identifies nodes ...
INTRODUCTION. The term knowledge graph (KG) has gained several different meanings across a range of usage scenarios. This paper focuses on the use of KGs in the context of two important current trends: the desire and need to harness the large and diverse data that are now available and the advent of new machine learning capabilities for extracting meaning from unstructured text and images.
Some research 29,30 already addressed the raffination of the so-called 'hairball graph' which is overly dominated by unimportant edges. We prefer to use a custom raffination at this step ...
images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) ... Lee et al. (2018a) provide a review over graph attention models. The paper proposed by Yang et al. (2020) focuses on heterogeneous graph representation learning, where nodes or edges are of multiple types. Huang et al. (2020) review ...
Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph ...
In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We ...
One main research direction is to define graph convolutions from the spectral perspective, and thus, graph signal processing, such as graph filtering and graph wavelets, has attracted lots of research interests. ... One application on meshes which we consider in this paper is the shape correspondence, i.e., to find correspondences between ...
f this graph is not F-free, then do this step again.Step 2 Generate a random number. between 1 and 10, and repeat the next step r times.Step 3 Add a vertex v to G and rand. mly generate edges be-tween v and the vertices of G. If the resulting graph is not F-free, then remove the edges incident to v and generate th.
Recently, graph neural networks (GNNs) have become a hot topic in machine learning community. This paper presents a Scopus-based bibliometric overview of the GNNs' research since 2004 when GNN papers were first published. The study aims to evaluate GNN research trends, both quantitatively and qualitatively.
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. ... On graph pairs, we are able to ...
This paper is available from the Center for Research in Computing Technology, Division of Applied Sciences, Harvard University as Technical Report TR-19-94. 1 Introduction In a graph-bisection ...
Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm's execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis.
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the ...
A new feature on arXiv.org helps readers explore related academic papers directly from article abstract pages. Developed by Connected Papers and now released as an arXivLabs collaboration, the tool links to interactive visualizations of similar articles. Connected Papers graphs can help readers explore a visual overview of a new academic field, create a bibliography, discover the most relevant ...
By Dr. Sowndarya Somasundaram. May 25, 2023. 7622. When explaining graphs in a thesis and research paper, it is essential to provide a clear and concise interpretation of the data represented in the graph. In this article, iLovePhD presented you with an effective guide to explain graphs in the thesis and research paper.
To create each graph, we analyze an order of ~50,000 papers and select the few dozen with the strongest connections to the origin paper (more on that below).
A systematic overview of deep learning technologies for forecasting and interpreting human events, with a primary focus on political events, and investigates recent achievements in graph neural networks, owing to the prevalence of relational data and the efficacy of graph learning models. Human events such as hospital visits, protests, and epidemic outbreaks directly affect individuals ...
Research Papers; Receive an update when the latest issues in this journal are published. Sign in to set up alerts. Research Papers. select article Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response.
By leveraging the data structure advantages of the knowledge graph, this research facilitates the rapid querying of PV potential assessment results for various scales, from Suzhou City down to Gusu District, Wumenqiao Street, or any other location.
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2 ...
Graph databases (DB) such as Neo4j and Arango DB are the straightforward choice. However, extending your tech stack by another db type and learning a new query language (e.g. Cypher/Gremlin) can be time-consuming. From my high-level research, there are also no great serverless options available.
Social network analysis is the process of investigating social structures through the use of networks and graph theory. It combines a variety of techniques for analyzing the structure of social networks as well as theories that aim at explaining the underlying dynamics and patterns observed in these structures. It is an inherently interdisciplinary field which originally emerged from the ...
Title: Any Graph is a Mapper Graph Authors: Enrique G Alvarado , Robin Belton , Kang-Ju Lee , Sourabh Palande , Sarah Percival , Emilie Purvine , Sarah Tymochko View a PDF of the paper titled Any Graph is a Mapper Graph, by Enrique G Alvarado and 6 other authors