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About 10,000 papers for “Line graph”

Line Graph

Keon‐Woong Moon · Use R! · 2016

Cited by 0

Line Graph

The SAGE Encyclopedia of Research Design · 2022

Cited by 0

Line Graph

Encyclopedia of Research Design · 2010

Cited by 0

Example line graph.

Erica Chenoweth (13162440), Andrew Hocking (13162443), Zoe Marks (13162446) · Figshare · 2022

Cited by 0Open access

Line graph explorer

Robert Kincaid, Heidi Lam · 2006

Scientific measurements are often depicted as line graphs. State-of-the-art high throughput systems in life sciences, telemetry and electronics measurement rapidly generate hundreds to thousands of such graphs. Despite the increasing volume and ubiquity of such data, few software systems provide efficient interactive management, navigation and exploratory analysis of large line graph collections. To address these issues, we have developed Line Graph Explorer (LGE). LGE is a novel and visually scalable line graph management system that supports facile navigation and interactive visual analysis. LGE provides a compact overview of the entire collection by encoding the y-dimension of individual line graphs with color instead of space, thus enabling the analyst to see major common features and alignments of the data. Using Focus+Context techniques, LGE provides interactions for viewing selected compressed graphs in detail as standard line graphs without losing a sense of the general pattern and major features of the collection. To further enhance visualization and pattern discovery, LGE provides sorting and clustering of line graphs based on similarity of selected graph features. Sequential sorting by associated line graph metadata is also supported. We illustrate the features and use of LGE with examples from meteorology and biology.

Cited by 82

Line Graph Links

Weiling Yang, Xian’an Jin, Fuji Zhang · Acta Mathematicae Applicatae Sinica English Series · 2021

Cited by 0

Plithogenic Line Graph, Star Graph, and Regular Graph

Takao Fujita · Plithogenic Logic and Computation · 2025

Graph theory, a branch of mathematics, studies relationships between entities using vertices and edges. In this field, Uncertain Graph Theory has emerged to capture the uncertainties inherent in real-world networks. While Turiyam Neutrosophic and Plithogenic Graphs are established within uncertain graph theory, concepts such as Line Graphs, Star Graphs, and Regular Graphs have not been extensively explored in these frameworks. This paper seeks to fill this gap by introducing and developing the concepts of Line Graph, Star Graph, and Regular Graph specifically within the contexts of Turiyam Neutrosophic and Plithogenic Graphs.

Cited by 0Open access

Molecular Graph Contrastive Learning with Line Graph

Xueyuan Chen, Shangzhe Li, Ruomei Liu et al. · ArXiv.org · 2025

Trapped by the label scarcity in molecular property prediction and drug design, graph contrastive learning (GCL) came forward. Leading contrastive learning works show two kinds of view generators, that is, random or learnable data corruption and domain knowledge incorporation. While effective, the two ways also lead to molecular semantics altering and limited generalization capability, respectively. To this end, we relate the \textbf{L}in\textbf{E} graph with \textbf{MO}lecular graph co\textbf{N}trastive learning and propose a novel method termed \textit{LEMON}. Specifically, by contrasting the given graph with the corresponding line graph, the graph encoder can freely encode the molecular semantics without omission. Furthermore, we present a new patch with edge attribute fusion and two local contrastive losses enhance information transmission and tackle hard negative samples. Compared with state-of-the-art (SOTA) methods for view generation, superior performance on molecular property prediction suggests the effectiveness of our proposed framework.

Cited by 0Open access

Line Graph Characterization of Cyclic Subgroup Graph

Malviy, Siddharth, Kakkar, Vipul, Srivastava, Swapnil · ArXiv.org · 2025

The cyclic subgroup graph ${\Gamma(G)}$ of a group $G$ is the simple undirected graph with cyclic subgroups as a vertex set and two distinct vertices $H_1$ and $H_2$ are adjacent if and only if $H_1 \leq H_2$ and there does not exist any cyclic subgroup $K$ such that $H_1 < K < H_2$. In this paper, we classify all the finite groups $G$ such that $\Gamma(G)$ is the line graph of some graph.

Cited by 0Open access

SHAP Value Line Graph.

Muhammad Idrees (92773), Maqbool Hussain Sial (20896546), Najam Ul Hassan (16865529) · Figshare · 2025

<div> The Stability of the economy is always a great challenge across the world, especially in under developed countries. Many researchers have contributed to forecasting the Stock Market and controlling the situation to ensure economic stability over the past several decades. For this purpose, many researchers have built various models and gained benefits. This journey continues to date and will persist for the betterment of the stock market. This study is also a part of this journey, where four learning-based models are tailored for stock price prediction. Daily business data from the Karachi Stock Exchange (100 Index), covering from February 22, 2008 to February 23, 2021, is used for training and testing these models. This paper presenting four deep learning models with different architectures, namely the Artificial Neural Network model, the Recurrent Neural Network with Attention model, the Long Short-Term Memory Network with Attention model, and the Gated Recurrent Unit with Attention model. The Long Short-Term Memory with attention model was found to be the top-performing technique for accurately predicting stock exchange prices. During the Training, Validation and Testing Sessions, we observed the R-Squared values of the proposed model to be 0.9996, 0.9980 and 0.9921, respectively, making it the best-performing model among those mentioned above. </div>

Cited by 0Open access

On-line graph coloring

Iwona Cieślik · Jagiellonian Digital Library (Jagiellonian University) · 2006

Cited by 3

Perplexity-Topic line graph.

Podong Song (20390102), Wiseong Jin (18271694), Shaowei Chen (1391854) et al. · Figshare · 2024

&lt;div&gt;&lt;p&gt;Cyberspace is emerging as a critical living environment, significantly influencing sustainable human development. Internet public opinion is a crucial aspect of cyberspace governance, serving as the most important form of expressing popular will. However, perceiving public opinion can be challenging due to its complex and elusive nature. In this paper, we propose a novel framework for perceiving popular will, managing public opinion, and influencing people’s behavior, based on machine learning and game theory approaches. Our framework leverages deep learning techniques to analyze public opinion, active learning methods to reduce costs, and game theory to make optimal management decisions. We verify the effectiveness of our framework using empirical data collected from Chinese provinces Y and G, and provide theoretical support by analyzing the interrelationship between public opinion, online public opinion, and people’s behavior. Our framework can be applied inexpensively to studies in other regions, thereby offering valuable insights into cyberspace governance and public opinion management.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access