scholarly journals Dynamic graph exploration by interactively linked node-link diagrams and matrix visualizations

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Michael Burch ◽  
Kiet Bennema ten Brinke ◽  
Adrien Castella ◽  
Ghassen Karray Sebastiaan Peters ◽  
Vasil Shteriyanov ◽  
...  
Keyword(s):  
Visual Outcome ◽  
Automatic Identification ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Additional Time ◽  
Graph Exploration ◽  
Visual Metaphors ◽  
Link Diagrams ◽  
Dense Graphs ◽  
User Experiment

AbstractThe visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property. For sparse and small graphs, the most efficient approach to such visualization is node-link diagrams, whereas for dense graphs with attached data, adjacency matrices might be the better choice. Because graphs can contain both properties, being globally sparse and locally dense, a combination of several visual metaphors as well as static and dynamic visualizations is beneficial. In this paper, a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described. As the novelty of this technique, insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views. Moreover, the importance of nodes and node groups can be detected, computed, and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes. As an additional feature set, an automatic identification of groups, clusters, and outliers is provided over time, and based on the visual outcome of the node-link and matrix visualizations, the repertoire of the supported layout and matrix reordering techniques is extended, and more interaction techniques are provided when considering the dynamics of the graph data. Finally, a small user experiment was conducted to investigate the usability of the proposed approach. The usefulness of the proposed tool is illustrated by applying it to a graph dataset, such as e co-authorships, co-citations, and a Comprehensible Perl Archive Network distribution.

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.

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 Perception of Animated Node-Link Diagrams for Dynamic Graphs

2012 ◽  
Vol 31 (3pt3) ◽  
pp. 1205-1214 ◽  
Author(s):  
S. Ghani ◽  
N. Elmqvist ◽  
J. S. Yi
Keyword(s):  
Dynamic Graphs ◽  
Link Diagrams

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.

scholarly journals AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN

2019 ◽  
Author(s):  
Li Zheng ◽  
Zhenpeng Li ◽  
Jian Li ◽  
Zhao Li ◽  
Jun Gao
Keyword(s):  
Anomaly Detection ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Short Term ◽  
Convolutional Network ◽  
Attention Model ◽  
Temporal Features ◽  
Structural Content ◽  
Real World Datasets

Anomaly detection in dynamic graphs becomes very critical in many different application scenarios, e.g., recommender systems, while it also raises huge challenges due to the high flexible nature of anomaly and lack of sufficient labelled data. It is better to learn the anomaly patterns by considering all possible features including the structural, content and temporal features, rather than utilizing heuristic rules over the partial features. In this paper, we propose AddGraph, a general end-to-end anomalous edge detection framework using an extended temporal GCN (Graph Convolutional Network) with an attention model, which can capture both long-term patterns and the short-term patterns in dynamic graphs. In order to cope with insufficient explicit labelled data, we employ the negative sampling and margin loss in training of AddGraph in a semi-supervised fashion. We conduct extensive experiments on real-world datasets, and illustrate that AddGraph can outperform the state-of-the-art competitors in anomaly detection significantly.

Managing dynamic graphs of mobile computer networks with random disturbances

2020 ◽  
pp. 136-142 ◽  
Author(s):  
A.I. Mikov ◽  
A.A. Mikov
Keyword(s):  
Computer Networks ◽  
Transition Period ◽  
Rapid Change ◽  
External Control ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Mobile Computer ◽  
Quality Of Control ◽  
Random Disturbances

We consider mobile computer networks whose stationary movements are forced to change at some points in time by external control actions or random disturbances. External influence can be aimed at the transition of the network to another stationary movement, but during the transition period there is a danger of decreasing the reliability of the network, the quality of its functioning, and even the threat of operability. One of the reasons for this is the rapid change in the topological (graph) characteristics of the network — the loss of connectivity of the dynamic graph of the network, the change in the set of available data transmission routes between nodes. The paper describes the results of statistical modeling of the characteristics of dynamic graphs for perturbed motion, and also presents algorithms for managing dynamic graphs during the transition process, which reduce the negative effects on the network from external influences. Using methods of predictive modeling, the quality of control algorithms is evaluated.

Enhancing Game Agent Pathfinding Through Dynamic Graph Reweighting

2019 ◽  
Vol 34 (04) ◽  
pp. 2059010
Author(s):  
Paweł Stawarz
Keyword(s):  
Video Game ◽  
Fine Tuning ◽  
Dynamic Graph ◽  
Time And Space ◽  
Additional Time

This paper proposes a computationally inexpensive algorithm that utilizes player data to optimize nonplayer character pathfinding in a competitive, multiplayer environment and focuses on imitating the player behavior. The algorithm’s input consists of player statistics gathered during the current and previous matches with additional time and space context, similar in design to influence maps. The input is then enriched with two additional, novel variables, allowing easy online fine-tuning of the output. The obtained result influences the final edge values of the map graph. Any known pathfinding algorithm that works with digraphs can then be utilized to control the agent. This paper contains exemplary results obtained when analyzing input on a map modeled after an existing map in the video game Unreal Tournament.

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