We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions.
Graph-based deep learning for communication networks: A …
Mar 1, 2022 · In this survey, we review the rapidly growing body of research using different graph-based deep learning models, e.g. graph convolutional and graph attention networks, in various problems from different types of communication networks, e.g. wireless networks, wired networks, and software defined networks.
Communication Graph - an overview | ScienceDirect Topics
A 'Communication Graph' is defined as a type of graph in which nodes represent entities like routers or transceiver pairs, and edges represent connections like physical links or interference relationships in communication networks.
Task-Oriented Communication for Graph Data: A Graph …
Sep 23, 2024 · This paper introduces a method to extract a smaller, task-focused subgraph that maintains key information while reducing communication overhead. Our approach utilizes graph neural networks (GNNs) and the graph information bottleneck (GIB) principle to create a compact, informative, and robust graph representation suitable for transmission.
Semantic Communications Toward Graph Data - IEEE Xplore
To address this problem, this article introduces a topology-aware graph semantic coding (TAGSC) framework. The framework emphasizes the critical need for understanding graph topology to effectively transmit and interpret graph semantic information.
Our experiments show that this GIB-based method significantly lowers communication costs while preserving essential task-related information. The approach demonstrates robust perfor-mance across various communication channels, suitable for both continuous and discrete systems.
Communication Graph - an overview | ScienceDirect Topics
In communication, graphs are used to represent the networks, data organization flow of computations and computational devices. Based on graph theory, channel sharing scheme is proposed in Zhang et al. (2013) which coordinates the D2D pairs and the cellular UEs in order to increase the capacity of a system.
(PDF) Communication Structures, its graph representation and ...
Dec 1, 2020 · Communication networks are modelled using graph structures. The efficiency of a network can be determined using the parameters of the graph model. The article reviews the modelling of various...
Graphs are an essential data type to generate structured representations of many real-life elements, such as molecules in chemistry, gravitational systems in physics, proteins in biology, or user relations in social networks. In the field of communication networks, graphs are pervasively used to represent many fundamental network components ...
In this paper we develop2 a multi-agent epistemic logic with a communication modality where agents are assumed to communicate according to some fixed communication graph.
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