Results 91 to 100 of about 1,167,245 (202)

Fundamental Groups of Hamming Graphs

The PUMP Journal of Undergraduate Research
Recently there has been growing interest in discrete homotopies and homotopies of graphs beyond treating graphs as 1-dimensional simplicial spaces. One such type of homotopy is the -homotopy. Recent work by Chih-Scull has developed a homotopy category, a fundamental group for graphs under this homotopy, and a way of computing covers of graphs that lift
Behal, Keira, Chih, Tien
openaire   +2 more sources

Node embedding approach for accurate detection of fake reviews: a graph-based machine learning approach with explainable AI

International Journal of Data Science and Analysis
In recent years, online reviews have become increasingly important in promoting various products and services. Unfortunately, writing deceptive reviews has also become a common practice to promote one’s own business or tarnish the reputation of ...
Nazar Zaki, Anusuya Krishnan, S. Turaev, Zahiriddin Rustamov, Jaloliddin Rustamov, Aisha Almusalami, Farah Ayyad, Tsion Regasa, Brice Boris Iriho   +8 more
semanticscholar   +1 more source

Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning

BMC Medical Informatics and Decision Making
Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality.
Hang Wu, Wenqi Shi, May D. Wang
doaj   +1 more source

Multi-Attribute Graph Estimation With Sparse-Group Non-Convex Penalties

IEEE Access
We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar ...
Jitendra K. Tugnait
doaj   +1 more source

Graph embeddings into Hamming spaces

, 2019
Graph embeddings deal with injective maps from a given simple, undirected graph $G=(V,E)$ into a metric space, such as $\mathbb{R}^n$ with the Euclidean metric. This concept is widely studied in computer science, see \cite{ge1}, but also offers attractive research in pure graph theory \cite{ge2}. In this note we show that any graph can be embedded into
openaire   +2 more sources

A method for quantifying the generalization capabilities of generative models for solving Ising models

Machine Learning: Science and Technology
For Ising models with complex energy landscapes, whether the ground state can be found by neural networks depends heavily on the Hamming distance between the training datasets and the ground state.
Qunlong Ma, Zhi Ma, Ming Gao
doaj   +1 more source

NAPE: Numbering as a Position Encoding in Graphs

IEEE Access
Deep learning has been instrumental in feature extraction from various data types, such as images and sequences, which inherently possess oriented structures.
Olayinka Ajayi, Hongkai Wen, Tanaya Guha
doaj   +1 more source

Minimum supports of eigenfunctions of Hamming graphs

Discrete Mathematics, 2017
in ...
openaire   +3 more sources

Eigenspaces of Hamming graphs and unitary Cayley graphs

Ars Mathematica Contemporanea, 2010
In this work, the eigenspaces of unitary Cayley graphs and certain Hamming graphs are considered. It is shown that these graph classes are closely related and admit particularly simple eigenspace bases for all eigenvalues, namely bases containing vectors only with entries from the set {0, 1, −1}.
openaire   +2 more sources

Classification of mammograms: Comparing a graphical to a geometrical approach

EngMedicine
Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammography, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC) lesions.
Anirban Ghosh, Priya Ranjan, Kumar Dron Shrivastav, Rajiv Janardhanan   +3 more
doaj   +1 more source