Graph theory ≈ Graph theory
View article
Knowledge Graph Embedding via Dynamic Mapping Matrix Open
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zhao. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers…
View article
Modular Brain Networks Open
The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined a…
View article
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review Open
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurologica…
View article
Discrete Signal Processing on Graphs: Sampling Theory Open
We propose a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory. We show that the perfect recovery is possible for graph signals bandlim…
View article
Graph theory methods: applications in brain networks Open
Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain networks, from molecular to behavioral scales, are ever increasing in size and complexity. These developments lead to a strong demand for appropriate t…
View article
Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations Open
Graph theoretical analysis has become an important tool in the examination of brain dysconnectivity in neurological and psychiatric brain disorders. A common analysis step in the construction of the functional graph or network involves "th…
View article
Learning Graphs From Data: A Signal Representation Perspective Open
The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural choice of the graph is not readily available from the data set…
View article
Network dismantling Open
Significance Many systems of interest can be represented by a network of nodes connected by edges. In many circumstances, the existence of a giant component is necessary for the network to fulfill its function. Motivated by the need to und…
View article
Merging the A-and Q-spectral theories Open
Let G be a graph with adjacency matrix A(G), and let D(G) be the diagonal matrix of the degrees of G: The signless Laplacian Q(G) of G is defined as Q(G):= A(G) +D(G). Cvetkovic called the study of the adjacency matrix the A-spectral theor…
View article
A Guide to Conquer the Biological Network Era Using Graph Theory Open
Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph t…
View article
Study of biological networks using graph theory Open
As an effective modeling, analysis and computational tool, graph theory is widely used in biological mathematics to deal with various biology problems. In the field of microbiology, graph can express the molecular structure, where cell, ge…
View article
A Graph Signal Processing Perspective on Functional Brain Imaging Open
Modern neuroimaging techniques provide us with unique views on brain structure and function; i.e., how the brain is wired, and where and when activity takes place. Data acquired using these techniques can be analyzed in terms of its networ…
View article
BRAPH: A graph theory software for the analysis of brain connectivity Open
The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory…
View article
Graph-based analysis of brain connectivity in schizophrenia Open
The present study evaluated brain connectivity using electroencephalography (EEG) data from 14 patients with schizophrenia and 14 healthy controls. Phase-Locking Value (PLV), Phase-Lag Index (PLI) and Directed Transfer Function (DTF) were …
View article
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Open
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explainable AI approaches are not applicable. To a large extent, G…
View article
Multi-Agent Trajectory Prediction With Heterogeneous Edge-Enhanced Graph Attention Network Open
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations. Two main challenges for this task are to han…
View article
A Simple Approach to Distributed Observer Design for Linear Systems Open
This note investigates the distributed estimation problem for continuous-time linear time-invariant (LTI) systems observed by a network of observers. Our starting point is a given LTI system whose full state vector we want to estimate, bas…
View article
Interpreting CNN Knowledge via an Explanatory Graph Open
This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of obje…
View article
Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world Open
Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer’s disease or depression have adapted …
View article
Large-scale DCMs for resting-state fMRI Open
This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with fun…
View article
Weighted Visibility Graph With Complex Network Features in the Detection of Epilepsy Open
Epilepsy detection from electrical characteristics of EEG signals obtained from the brain of undergone subject is a challenge task for both research and neurologist due to the non-stationary and chaotic nature of EEG signals. As epileptic …
View article
Graph Frequency Analysis of Brain Signals Open
This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and ima…
View article
Transferring Robustness for Graph Neural Network Against Poisoning Attacks Open
Graph neural networks (GNNs) are widely used in many applications. However,\ntheir robustness against adversarial attacks is criticized. Prior studies show\nthat using unnoticeable modifications on graph topology or nodal features can\nsig…
View article
Mapping Structural Connectivity Using Diffusion <span>MRI</span>: Challenges and Opportunities Open
Diffusion MRI‐based tractography is the most commonly‐used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory—a powerful mathematical approach …
View article
Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology Open
In a cascading power transmission outage, component outages propagate nonlocally; after one component outages, the next failure may be very distant, both topologically and geographically. As a result, simple models of topological contagion…
View article
Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model Open
Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNL…
View article
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective Open
Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functi…
View article
Changes of Functional Brain Networks in Major Depressive Disorder: A Graph Theoretical Analysis of Resting-State fMRI Open
Recent developments in graph theory have heightened the need for investigating the disruptions in the topological structure of functional brain network in major depressive disorder (MDD). In this study, we employed resting-state functional…
View article
Semantically Smooth Knowledge Graph Embedding Open
Shu Guo, Quan Wang, Bin Wang, Lihong Wang, Li Guo. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2…
View article
Graph Theoretical Framework of Brain Networks in Multiple Sclerosis: A Review of Concepts Open
Network science provides powerful access to essential organizational principles of the human brain. It has been applied in combination with graph theory to characterize brain connectivity patterns. In multiple sclerosis (MS), analysis of t…