scholarly journals State-based network similarity visualization

2019 ◽  
Vol 19 (2) ◽  
pp. 96-113
Author(s):  
Sugeerth Murugesan ◽  
Kristofer Bouchard ◽  
Jesse Brown ◽  
Mariam Kiran ◽  
Dan Lurie ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Time Interval ◽  
Complex Dynamic ◽  
Temporal Phase ◽  
Topology Changes ◽  
Network Similarity ◽  
Summary Representation ◽  
Interactive Visual Analysis

We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. We present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.

scholarly journals The (T, L)-Path Model and Algorithms for Information Dissemination in Dynamic Networks

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 212
Author(s):  
Zhiwei Yang ◽  
Weigang Wu
Keyword(s):  
Network Topology ◽  
Information Dissemination ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Models ◽  
Point Of View ◽  
Path Model ◽  
Dynamic Network Models ◽  
Topology Changes ◽  
Practical Design

A dynamic network is the abstraction of distributed systems with frequent network topology changes. With such dynamic network models, fundamental distributed computing problems can be formally studied with rigorous correctness. Although quite a number of models have been proposed and studied for dynamic networks, the existing models are usually defined from the point of view of connectivity properties. In this paper, instead, we examine the dynamicity of network topology according to the procedure of changes, i.e., how the topology or links change. Following such an approach, we propose the notion of the “instant path” and define two dynamic network models based on the instant path. Based on these two models, we design distributed algorithms for the problem of information dissemination respectively, one of the fundamental distributing computing problems. The correctness of our algorithms is formally proved and their performance in time cost and communication cost is analyzed. Compared with existing connectivity based dynamic network models and algorithms, our procedure based ones are definitely easier to be instantiated in the practical design and deployment of dynamic networks.

scholarly journals Distributed Queuing in Dynamic Networks

2015 ◽  
Vol 25 (02) ◽  
pp. 1550005 ◽  
Author(s):  
Gokarna Sharma ◽  
Costas Busch
Keyword(s):  
Dynamic Networks ◽  
Dynamic Network ◽  
Connected Graphs ◽  
Network Nodes ◽  
Dynamic Network Model ◽  
Topology Changes ◽  
Communication Links ◽  
Minimum Number ◽  
Distributed Queue ◽  
Distributed Queueing

We consider the problem of forming a distributed queue in the synchronous dynamic network model of Kuhn, Lynch, and Oshman (STOC 2010) in which the network topology changes from round to round but the network stays connected. Queue requests may arrive over rounds at network nodes and the goal is to eventually enqueue them in a distributed queue. We show that in 1-interval connected graphs, where the communication links change arbitrarily between every round, it is possible to solve the distributed queueing problem in [Formula: see text] rounds using [Formula: see text] size messages, where [Formula: see text] is the number of nodes in the network and [Formula: see text] is the number of queue requests. Further, we show that for more stable graphs, e.g. [Formula: see text]-interval connected graphs where the communication links change in every [Formula: see text] rounds, the distributed queuing problem can be solved in [Formula: see text] rounds using the same [Formula: see text] size messages, where [Formula: see text] is the concurrency level parameter that captures the minimum number of active queue requests in the system at any round. These results hold in any arbitrary arrival of queue requests and ensure correctness of the queue formed. To our best knowledge, these are the first solutions to the distributed queuing problem in highly dynamic networks.

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.

scholarly journals ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms

2021 ◽  
Author(s):  
Hamid Mansoor ◽  
Walter Gerych ◽  
Abdulaziz Alajaji ◽  
Luke Buquicchio ◽  
Kavin Chandrasekaran ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Behavioral Rhythms ◽  
Interactive Visual Analysis

scholarly journals Interactive Visual Analysis of Set-Typed Data

2008 ◽  
Vol 14 (6) ◽  
pp. 1340-1347 ◽  
Author(s):  
W. Freiler ◽  
K. Matkovic ◽  
H. Hauser
Keyword(s):  
Visual Analysis ◽  
Interactive Visual Analysis

An experimental evaluation and guideline for path finding in weighted dynamic network

2021 ◽  
Vol 14 (11) ◽  
pp. 2127-2140
Author(s):  
Mengxuan Zhang ◽  
Lei Li ◽  
Xiaofang Zhou
Keyword(s):  
Shortest Path ◽  
State Of The Art ◽  
Dynamic Networks ◽  
Real Life ◽  
Dynamic Network ◽  
Problem Space ◽  
Network Applications ◽  
Complete Problem ◽  
Path Computation ◽  
Shortest Path Computation

Shortest path computation is a building block of various network applications. Since real-life networks evolve as time passes, the Dynamic Shortest Path (DSP) problem has drawn lots of attention in recent years. However, as DSP has many factors related to network topology, update patterns, and query characteristics, existing works only test their algorithms on limited situations without sufficient comparisons with other approaches. Thus, it is still hard to choose the most suitable method in practice. To this end, we first identify the determinant dimensions and constraint dimensions of the DSP problem and create a complete problem space to cover all possible situations. Then we evaluate the state-of-the-art DSP methods under the same implementation standard and test them systematically under a set of synthetic dynamic networks. Furthermore, we propose the concept of dynamic degree to classify the dynamic environments and use throughput to evaluate their performance. These results can serve as a guideline to find the best solution for each situation during system implementation and also identify research opportunities. Finally, we validate our findings on real-life dynamic networks.

Data Mining Techniques for Communities’ Detection in Dynamic Social Networks

Data Mining ◽  
2013 ◽  
pp. 719-733
Author(s):  
Céline Robardet
Keyword(s):  
Dynamic Networks ◽  
Dynamic Network ◽  
Added Value ◽  
Strong Interactions ◽  
Time Stamp ◽  
Efficient Computation ◽  
Real World Data ◽  
Positive Side ◽  
Dynamic Social Networks ◽  
Temporal Events

Social network analysis studies relationships between individuals and aims at identifying interesting substructures such as communities. This type of network structure is intuitively defined as a subset of nodes more densely linked, when compared with the rest of the network. Such dense subgraphs gather individuals sharing similar property depending on the type of relation encoded in the graph. In this chapter we tackle the problem of identifying communities in dynamic networks where relationships among entities evolve over time. Meaningful patterns in such structured data must capture the strong interactions between individuals but also their temporal relationships. We propose a pattern discovery method to identify evolving patterns defined by constraints. In this paradigm, constraints are parameterized by the user to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. In the proposed approach, dense and isolated subgraphs, defined by two user-parameterized constraints, are first computed in the dynamic network restricted at a given time stamp. Second, the temporal evolution of such patterns is captured by associating a temporal event types to each subgraph. We consider five basic temporal events: the formation, dissolution, growth, diminution and stability of subgraphs from one time stamp to the next one. We propose an algorithm that finds such subgraphs in a time series of graphs processed incrementally. The extraction is feasible thanks to efficient pruning patterns strategies. Experimental results on real-world data confirm the practical feasibility of our approach. We evaluate the added-value of the method, both in terms of the relevancy of the extracted evolving patterns and in terms of scalability, on two dynamic sensor networks and on a dynamic mobility network.

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