scholarly journals EXPLORATION WITH RETURN OF HIGHLY DYNAMIC NETWORKS

2021 ◽  
Vol 9 (10) ◽  
pp. 315-319
Author(s):  
Ahmed Mouhamadou Wade ◽  
Keyword(s):  
Dynamic Networks ◽  
Dynamic Graphs ◽  
Sufficient Time ◽  
Dynamic Graph ◽  
Necessary And Sufficient

In this paper, we study the necessary and sufficient time to explore with return constantly connected dynamic networks modelled by a dynamic graphs. Exploration with return consists, for an agent operating in a dynamic graph, of visiting all the vertices of the graph and returning to the starting vertex. We show that for constantly connected dynamic graphs based on a ring of sizen,3n-4 time units are necessary and sufficient to explore it. Assuming that the agent knows the dynamics of the graph.

scholarly journals A Survey on Embedding Dynamic Graphs

2023 ◽  
Vol 55 (1) ◽  
pp. 1-37
Author(s):  
Claudio D. T. Barros ◽  
Matheus R. F. Mendonça ◽  
Alex B. Vieira ◽  
Artur Ziviani
Keyword(s):  
Link Prediction ◽  
Point Processes ◽  
Graph Embedding ◽  
Graph Visualization ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Topological Evolution ◽  
Evolution Feature ◽  
Feature Evolution ◽  
And Diffusion

Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature evolution, and diffusion. Therefore, several methods for embedding dynamic graphs have been proposed to learn network representations over time, facing novel challenges, such as time-domain modeling, temporal features to be captured, and the temporal granularity to be embedded. In this survey, we overview dynamic graph embedding, discussing its fundamentals and the recent advances developed so far. We introduce the formal definition of dynamic graph embedding, focusing on the problem setting and introducing a novel taxonomy for dynamic graph embedding input and output. We further explore different dynamic behaviors that may be encompassed by embeddings, classifying by topological evolution, feature evolution, and processes on networks. Afterward, we describe existing techniques and propose a taxonomy for dynamic graph embedding techniques based on algorithmic approaches, from matrix and tensor factorization to deep learning, random walks, and temporal point processes. We also elucidate main applications, including dynamic link prediction, anomaly detection, and diffusion prediction, and we further state some promising research directions in the area.

scholarly journals DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 11924-11931
Author(s):  
Zhongwei Qiu ◽  
Kai Qiu ◽  
Jianlong Fu ◽  
Dongmei Fu
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Semantic Relations ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Convolutional Network ◽  
Bottom Up ◽  
Multi Level ◽  
Human Pose ◽  
Relative Gains

Multi-person pose estimation aims to detect human keypoints from images with multiple persons. Bottom-up methods for multi-person pose estimation have attracted extensive attention, owing to the good balance between efficiency and accuracy. Recent bottom-up methods usually follow the principle of keypoints localization and grouping, where relations between keypoints are the keys to group keypoints. These relations spontaneously construct a graph of keypoints, where the edges represent the relations between two nodes (i.e., keypoints). Existing bottom-up methods mainly define relations by empirically picking out edges from this graph, while omitting edges that may contain useful semantic relations. In this paper, we propose a novel Dynamic Graph Convolutional Module (DGCM) to model rich relations in the keypoints graph. Specifically, we take into account all relations (all edges of the graph) and construct dynamic graphs to tolerate large variations of human pose. The DGCM is quite lightweight, which allows it to be stacked like a pyramid architecture and learn structural relations from multi-level features. Our network with single DGCM based on ResNet-50 achieves relative gains of 3.2% and 4.8% over state-of-the-art bottom-up methods on COCO keypoints and MPII dataset, respectively.

Community Fabric: Visualizing communities and structure in dynamic networks

2021 ◽  
pp. 147387162110560
Author(s):  
Evan Ezell ◽  
Seung-Hwan Lim ◽  
David Anderson ◽  
Robert Stewart
Keyword(s):  
Dynamic Networks ◽  
Dynamic Network ◽  
Data Sets ◽  
Dynamic Graphs ◽  
Visualization Technique ◽  
Web Based ◽  
New Approach ◽  
Community Evolution ◽  
Underlying Network ◽  
Flow Diagrams

We present Community Fabric, a novel visualization technique for simultaneously visualizing communities and structure within dynamic networks. In dynamic networks, the structure of the network is continuously evolving throughout time and these underlying topological shifts tend to lead to communal changes. Community Fabric helps the viewer more easily interpret and understand the interplay of structural change and community evolution in dynamic graphs. To achieve this, we take a new approach, hybridizing two popular network and community visualizations. Community Fabric combines the likes of the Biofabric static network visualization method with traditional community alluvial flow diagrams to visualize communities in a dynamic network while also displaying the underlying network structure. Our approach improves upon existing state-of-the-art techniques in several key areas. We describe the methodologies of Community Fabric, implement the visualization using modern web-based tools, and apply our approach to three example data sets.

scholarly journals Managing large distributed dynamic graphs for smart city network applications

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Nadav Voloch ◽  
Noa Voloch - Bloch ◽  
Yair Zadok
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Directed Graphs ◽  
Large City ◽  
Dynamic Graphs ◽  
Distributed Monitoring ◽  
Dynamic Graph ◽  
Local Conditions ◽  
Network Applications ◽  
On Line

AbstractSmart cities and traffic applications can be modelled by dynamic graphs for which vertices or edges can be added, removed or change their properties. In the smart city or traffic monitoring problem, we wish to detect if a city dynamic graph maintains a certain local or global property. Monitoring city large dynamic graphs, is even more complicated. To treat the monitoring problem efficiently we divide a large city graph into sub-graphs. In the distributed monitoring problem we would like to define some local conditions for which the global city graph G maintains a certain property. Furthermore, we would like to detect if a local city change in a sub-graph affect a global graph property. Here we show that turning the graph into a non-trivial one by handling directed graphs, weighted graphs, graphs with nodes that contain different attributes or combinations of these aspects, can be integrated in known urban environment applications. These implementations are demonstrated here in two types of network applications: traffic network application and on-line social network smart city applications. We exemplify these two problems, show their experimental results and characterize efficient monitoring algorithms that can handle them.

scholarly journals Shortest, Fastest, and Foremost Broadcast in Dynamic Networks

2015 ◽  
Vol 26 (04) ◽  
pp. 499-522 ◽  
Author(s):  
Arnaud Casteigts ◽  
Paola Flocchini ◽  
Bernard Mans ◽  
Nicola Santoro
Keyword(s):  
Dynamic Networks ◽  
Network Evolution ◽  
Recurrence Time ◽  
Dynamic Graphs ◽  
Specific Knowledge ◽  
Minimum Duration ◽  
Full Knowledge ◽  
Distinct Features ◽  
Over Time ◽  
Selection Of

Highly dynamic networks rarely offer end-to-end connectivity at a given time. Yet, connectivity in these networks can be established over time and space, based on temporal analogues of multi-hop paths (also called journeys). Attempting to optimize the selection of the journeys in these networks naturally leads to the study of three cases: shortest (minimum hop), fastest (minimum duration), and foremost (earliest arrival) journeys. Efficient centralized algorithms exists to compute all cases, when the full knowledge of the network evolution is given. In this paper, we study the distributed counterparts of these problems, i.e. shortest, fastest, and foremost broadcast with termination detection (TDB), with minimal knowledge on the topology. We show that the feasibility of each of these problems requires distinct features on the evolution, through identifying three classes of dynamic graphs wherein the problems become gradually feasible: graphs in which the re-appearance of edges is recurrent (class [Formula: see text]), bounded-recurrent ([Formula: see text]), or periodic ([Formula: see text]), together with specific knowledge that are respectively n (the number of nodes), Δ (a bound on the recurrence time), and p (the period). In these classes it is not required that all pairs of nodes get in contact — only that the overall footprint of the graph is connected over time. Our results, together with the strict inclusion between [Formula: see text], [Formula: see text], and [Formula: see text], implies a feasibility order among the three variants of the problem, i.e. TDB[foremost] requires weaker assumptions on the topology dynamics than TDB[shortest], which itself requires less than TDB[fastest]. Reversely, these differences in feasibility imply that the computational powers of [Formula: see text], [Formula: see text], and [Formula: see text] also form a strict hierarchy.

Effective Temporal Graph Layout: A Comparative Study of Animation versus Static Display Methods

2011 ◽  
Vol 10 (1) ◽  
pp. 47-64 ◽  
Author(s):  
Michael Farrugia ◽  
Aaron Quigley
Keyword(s):  
Graph Drawing ◽  
Dynamic Networks ◽  
Experimental System ◽  
Graph Layout ◽  
Dynamic Graphs ◽  
Social Scientists ◽  
Web Based ◽  
Time Performance ◽  
Level Information ◽  
Micro Level

Graph drawing algorithms have classically addressed the layout of static graphs. However, the need to draw evolving or dynamic graphs has brought into question many of the assumptions, conventions and layout methods designed to date. For example, social scientists studying evolving social networks have created a demand for visual representations of graphs changing over time. Two common approaches to represent temporal information in graphs include animation of the network and use of static snapshots of the network at different points in time. Here, we report on two experiments, one in a laboratory environment and another using an asynchronous remote web-based platform, Mechanical Turk, to compare the efficiency of animated displays versus static displays. Four tasks are studied with each visual representation, where two characterise overview level information presentation, and two characterise micro level analytical tasks. For the tasks studied in these experiments and within the limits of the experimental system, the results of this study indicate that static representations are generally more effective particularly in terms of time performance, when compared to fully animated movie representations of dynamic networks.

scholarly journals Online summarization of dynamic graphs using subjective interestingness for sequential data

2020 ◽  
Author(s):  
Sarang Kapoor ◽  
Dhish Kumar Saxena ◽  
Matthijs van Leeuwen
Keyword(s):  
Real World ◽  
Information Gain ◽  
Current Knowledge ◽  
Maximum Entropy Principle ◽  
Sequential Data ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Real World Data ◽  
Mdl Principle ◽  
Subjective Interestingness

Abstract Many real-world phenomena can be represented as dynamic graphs, i.e., networks that change over time. The problem of dynamic graph summarization, i.e., to succinctly describe the evolution of a dynamic graph, has been widely studied. Existing methods typically use objective measures to find fixed structures such as cliques, stars, and cores. Most of the methods, however, do not consider the problem of online summarization, where the summary is incrementally conveyed to the analyst as the graph evolves, and (thus) do not take into account the knowledge of the analyst at a specific moment in time. We address this gap in the literature through a novel, generic framework for subjective interestingness for sequential data. Specifically, we iteratively identify atomic changes, called ‘actions’, that provide most information relative to the current knowledge of the analyst. For this, we introduce a novel information gain measure, which is motivated by the minimum description length (MDL) principle. With this measure, our approach discovers compact summaries without having to decide on the number of patterns. As such, we are the first to combine approaches for data mining based on subjective interestingness (using the maximum entropy principle) with pattern-based summarization (using the MDL principle). We instantiate this framework for dynamic graphs and dense subgraph patterns, and present DSSG, a heuristic algorithm for the online summarization of dynamic graphs by means of informative actions, each of which represents an interpretable change to the connectivity structure of the graph. The experiments on real-world data demonstrate that our approach effectively discovers informative summaries. We conclude with a case study on data from an airline network to show its potential for real-world applications.

Visual Stability in Dynamic Graph Drawings

i-com ◽  
2015 ◽  
Vol 14 (3) ◽  
Author(s):  
Alfredo Ramos Lezama ◽  
Irene-Angelica Chounta ◽  
Tilman Göhnert ◽  
H. Ulrich Hoppe
Keyword(s):  
Visual Search ◽  
Eye Tracking ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Visual Stability ◽  
Dynamic Graph ◽  
Proposed Model ◽  
Special Challenge ◽  
Tracking Device ◽  
Constant Shape

AbstractIn graph visualizations, dynamic networks are a special challenge. A typical approach is visualizing the network at several points in time. Drawing these individual time slices often leads to changes in the layout that distract viewers from important information about individual nodes. In this article, we present a mathematical model to quantify the visual stability of dynamic graph drawings. The model takes into account structural and layout-oriented characteristics of the graphs. In order to validate the model, we conducted a study using questionnaires and an eye-tracking device. The participants were asked to track nodes in a dynamic network with three different methods. Then, we compared these methods based on the proposed model, user feedback (questionnaires) and behavioral data (eye-tracking). The results suggest that dynamic graph drawings which assign a fixed position on the canvas to every actor in the network improve the efficiency of the visual search. Nonetheless, more time is required to process the image. In contrast to that, those dynamic graph drawings with a constant shape or with a minimal number of changes require less time to process the image but lose efficiency of visual search.

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