scholarly journals Visualisation of Long in Time Dynamic Networks on Large Touch Displays

2021 ◽  
Author(s):  
◽  
Alexandra Lee
Keyword(s):  
Laboratory Study ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Data ◽  
User Evaluation ◽  
Time Dynamic ◽  
Time Interaction ◽  
Expert User ◽  
Novel Method ◽  
Over Time

Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging fields from travel networks to epidemiology.However, it is very difficult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We first introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ‘interactive timeslicing’. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To confirm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we find that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also find that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the first, but this time using standard-sized displays with indirect mouse input. The second study reaffirms the results of the first, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types.

scholarly journals Epidemic Source Detection over Dynamic Networks

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1018
Author(s):  
Jaeyoung Choi
Keyword(s):  
Dynamic Model ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Estimation Algorithm ◽  
Estimation Methods ◽  
Source Detection ◽  
Source Estimation ◽  
Contact Information ◽  
Node Connectivity ◽  
Over Time

Epidemic source detection is one of the most crucial problems in statistical inference. For example, currently, the debate continues to reveal when and where the first spread of COVID-19 occured. For this problem, most of the works have assumed a static network topology, that is, the connections between nodes do not change over time. This is impractical because many nodes have some mobility in the network, or the connections can be changed. In this paper, we focus on the dynamic network, in the sense that the node connectivity varies over time. We first introduce a simple dynamic model, named k-flip dynamic such that k > 0 connections in the network may be changed with some probability at each time. Next, we design a proper estimation algorithm using some investigation for the contact information between infected nodes, named dynamic network source estimation (DNSE)(k) for the dynamic model. We perform various simulations for the algorithm compared to several existing source estimation methods. Our results show that the proposed algorithm outperforms and is efficient for finding the epidemic source compared to other methods. Further, we see that the detection probability for our proposed algorithm can be above 45% when we use budget to investigate the contact information from the infected nodes under some practical setting of k.

scholarly journals Coreness Variation Rule and Fast Updating Algorithm for Dynamic Networks

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 477 ◽  
Author(s):  
Liang Gao ◽  
Ge Gao ◽  
Dandan Ma ◽  
Lida Xu
Keyword(s):  
Decomposition Method ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Operator Method ◽  
Decomposition Algorithm ◽  
Symmetric Pair ◽  
Novel Method ◽  
Variation Rule

Coreness is one of the important indicators to measure the importance of a node. Traditionally, the coreness of a node is measured by k-core decomposition. However, to measure the coreness in a dynamic network, the k-core decomposition method becomes very time-consuming and inefficient, and cannot meet the need in very large real networks. Recently, the H operator method was proposed to calculate the coreness of a node, which provides a novel method to deal with the coreness of a node in a network. In this paper, we decode the coreness variation rule by a symmetric pair of experiments, i.e., deleting and adding edge, on real networks. Then, an algorithm to fast update the coreness of related nodes is proposed. Results on five real networks showed that the performance of the proposed algorithm was greatly enhanced and comprehensively superior to the k-core decomposition algorithm. Our study provides a promising way to optimize the algorithm of coreness calculation in the dynamic networks.

Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks

2022 ◽  
Vol 40 (3) ◽  
pp. 1-21
Author(s):  
Lili Wang ◽  
Chenghan Huang ◽  
Ying Lu ◽  
Weicheng Ma ◽  
Ruibo Liu ◽  
...  
Keyword(s):  
Real World ◽  
User Behavior ◽  
Dynamic Networks ◽  
General Purpose ◽  
Local Context ◽  
Structural Role ◽  
Time Dynamic ◽  
Evolving Networks ◽  
Structural Identity ◽  
Novel Method

Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them without extensive feature engineering. Prior work has shown how to generate network embeddings while preserving the structural role proximity of nodes. These methods, however, cannot capture the temporal evolution of the structural identity of the nodes in dynamic networks. Other works, on the other hand, have focused on learning microscopic dynamic embeddings. Though these methods can learn node representations over dynamic networks, these representations capture the local context of nodes and do not learn the structural roles of nodes. In this article, we propose a novel method for learning structural node embeddings in discrete-time dynamic networks. Our method, called HR2vec , tracks historical topology information in dynamic networks to learn dynamic structural role embeddings. Through experiments on synthetic and real-world temporal datasets, we show that our method outperforms other well-known methods in tasks where structural equivalence and historical information both play important roles. HR2vec can be used to model dynamic user behavior in any networked setting where users can be represented as nodes. Additionally, we propose a novel method (called network fingerprinting) that uses HR2vec embeddings for modeling whole (or partial) time-evolving networks. We showcase our network fingerprinting method on synthetic and real-world networks. Specifically, we demonstrate how our method can be used for detecting foreign-backed information operations on Twitter.

Use of breathable silicone technology in an ostomy appliance flange

2021 ◽  
Vol 30 (Sup8) ◽  
pp. 25-35
Author(s):  
Peta Lager ◽  
Lisa Loxdale
Keyword(s):  
Preliminary Data ◽  
Transepidermal Water Loss ◽  
User Evaluation ◽  
Skin Damage ◽  
Healthcare Use ◽  
Skin Injury ◽  
Moisture Management ◽  
Peristomal Skin ◽  
Medical Adhesive ◽  
Over Time

Leaks and peristomal skin complications are highly prevalent among people with a stoma, reported by over 80% of ostomates within 2 years of surgery. This suggests that there is room for improvement in ostomy appliances, particularly in their hydrocolloid-based adhesive flanges. Hydrocolloid has an absorptive method of moisture management that, over time, risks maceration and skin stripping, potentially leading to moisture-associated skin damage (MASD) and medical adhesive-related skin injury (MARSI). The newly developed Genii ostomy appliances (Trio Healthcare) use novel Sil2 Breathable Silicone Technology to provide secure, effective adhesion and manage moisture levels by replicating natural transepidermal water loss (TEWL). This has the potential to increase appliance wear time, reduce incidence of MASD and permit atraumatic removal without adhesive remover, reducing the risks of MARSI, as well as time burdens on the user and economic burdens on the healthcare system. Meanwhile, the silicone flanges and water-resistant sports fabric pouches are lightweight, flexible and unobtrusive, and they are the first appliances to be available in colours to match different skin tones, all of which provides security, comfort, confidence and discretion. This article explores the features of Sil2 and Genii ostomy appliances, with reference to preliminary data from a user evaluation.

scholarly journals DNA-based constitutional dynamic networks as functional modules for logic gates and computing circuit operations

2021 ◽  
Vol 12 (15) ◽  
pp. 5473-5483
Author(s):  
Zhixin Zhou ◽  
Jianbang Wang ◽  
R. D. Levine ◽  
Francoise Remacle ◽  
Itamar Willner
Keyword(s):  
Nucleic Acid ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Logic Gates ◽  
Functional Modules ◽  
Logic Operations

A nucleic acid-based constitutional dynamic network (CDN) provides a single functional computational module for diverse input-guided logic operations and computing circuits.

The dynamic nature of depression: a new micro-level perspective of mental disorder that meets current challenges

2013 ◽  
Vol 44 (7) ◽  
pp. 1349-1360 ◽  
Author(s):  
M. Wichers
Keyword(s):  
Dynamic Network ◽  
Building Blocks ◽  
Major Depressive ◽  
Dynamic Network Model ◽  
Healthy State ◽  
Behavioural Patterns ◽  
Micro Level ◽  
Continuous Dynamic ◽  
Dynamic Interplay ◽  
Over Time

The examination of moment-to-moment, ‘micro-level’ patterns of experience and behaviour using experience sampling methodology has contributed to our understanding of the ‘macro-level’ development of full-blown symptoms and disorders. This paper argues that the micro-level perspective can be used to identify the smallest building blocks underlying the onset and course of mental ill-health. Psychopathology may be the result of the continuous dynamic interplay between micro-level moment-to-moment experiences and behavioural patterns over time. Reinforcing loops between momentary states may alter the course of mental health towards either a more or less healthy state. An example with observed data, from a population of individuals with depressive symptoms, supports the validity of a dynamic network model of psychopathology and shows that together and over time, this continuous interplay between momentary states may result in the cluster of symptoms we call major depressive disorder. This approach may help conceptualize the nature of mental disorders, and generate individualized insights useful for diagnosis and treatment in psychiatry.

Motion estimation in vehicular environments based on Bayesian dynamic networks

2021 ◽  
pp. 1-12
Author(s):  
Lauro Reyes-Cocoletzi ◽  
Ivan Olmos-Pineda ◽  
J. Arturo Olvera-Lopez
Keyword(s):  
Real World ◽  
Dynamic Networks ◽  
Ground Truth ◽  
Change Of Direction ◽  
Prediction Rate ◽  
Different Types ◽  
Novel Method ◽  
Comparison Of The Results ◽  
Multiple Obstacles ◽  
Real Traffic

The cornerstone to achieve the development of autonomous ground driving with the lowest possible risk of collision in real traffic environments is the movement estimation obstacle. Predicting trajectories of multiple obstacles in dynamic traffic scenarios is a major challenge, especially when different types of obstacles such as vehicles and pedestrians are involved. According to the issues mentioned, in this work a novel method based on Bayesian dynamic networks is proposed to infer the paths of interest objects (IO). Environmental information is obtained through stereo video, the direction vectors of multiple obstacles are computed and the trajectories with the highest probability of occurrence and the possibility of collision are highlighted. The proposed approach was evaluated using test environments considering different road layouts and multiple obstacles in real-world traffic scenarios. A comparison of the results obtained against the ground truth of the paths taken by each detected IO is performed. According to experimental results, the proposed method obtains a prediction rate of 75% for the change of direction taking into consideration the risk of collision. The importance of the proposal is that it does not obviate the risk of collision in contrast with related work.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

Sign in / Sign up

Export Citation Format

Share Document