scholarly journals A Hierarchical and Abstraction-Based Blockchain Model

2019 ◽  
Vol 9 (11) ◽  
pp. 2343 ◽  
Author(s):  
Swagatika Sahoo ◽  
Akshay M. Fajge ◽  
Raju Halder ◽  
Agostino Cortesi
Keyword(s):  
Large Scale ◽  
Abstract Interpretation ◽  
Organizational Structures ◽  
Network Size ◽  
Small Scale ◽  
Time Interval ◽  
Huge Number ◽  
Large Scale Network ◽  
Regular Time ◽  
Scale Network

In the nine years since its launch, amid intense research, scalability is always a serious concern in blockchain, especially in case of large-scale network generating huge number of transaction-records. In this paper, we propose a hierarchical blockchain model characterized by: (1) each level maintains multiple local blockchain networks, (2) each local blockchain records local transactional activities, and (3) partial views (tunable w.r.t. precision) of different subsets of local blockchain-records are maintained in the blockchains at next level of the hierarchy. To meet this objective, we apply abstractions on a set of transaction-records in a regular time interval by following the Abstract Interpretation framework, which provides a tunable precision in various abstract domain and guarantees the soundness of the system. While this model suitably fits to the real-worlds organizational structures, the proposal is powerful enough to scale when large number of nodes participate in a network resulting into an enormous growth of the network-size and the number of transaction-records. We discuss experimental results on a small-scale network with three sub networks at lower-level and by abstracting the transaction-records in the abstract domain of intervals. The results are encouraging and clearly indicate the effectiveness of this approach to control exponential growth of blockchain size w.r.t. the total number of participants in the network.

Scalings of first-return time for random walks on generalized and weighted transfractal networks

2019 ◽  
Vol 33 (26) ◽  
pp. 1950306
Author(s):  
Qin Liu ◽  
Weigang Sun ◽  
Suyu Liu
Keyword(s):  
Random Walks ◽  
Large Scale ◽  
Probability Generating Function ◽  
Return Time ◽  
Network Size ◽  
Large Network ◽  
Large Scale Network ◽  
Scale Network ◽  
The Mean ◽  
First Return Time

The first-return time (FRT) is an effective measurement of random walks. Presently, it has attracted considerable attention with a focus on its scalings with regard to network size. In this paper, we propose a family of generalized and weighted transfractal networks and obtain the scalings of the FRT for a prescribed initial hub node. By employing the self-similarity of our networks, we calculate the first and second moments of FRT by the probability generating function and obtain the scalings of the mean and variance of FRT with regard to network size. For a large network, the mean FRT scales with the network size at the sublinear rate. Further, the efficiency of random walks relates strongly with the weight factor. The smaller the weight, the better the efficiency bears. Finally, we show that the variance of FRT decreases with more number of initial nodes, implying that our method is more effective for large-scale network size and the estimation of the mean FRT is more reliable.

scholarly journals Lightweight Label Propagation for Large-Scale Network Data

2018 ◽  
Author(s):  
De-Ming Liang ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Label Propagation ◽  
Stochastic Gradient Descent ◽  
Small Scale ◽  
Warm Start ◽  
Large Scale Data ◽  
Large Scale Network ◽  
Memory Overhead ◽  
Scale Network ◽  
Scale Data

Label propagation spreads the soft labels from few labeled data to a large amount of unlabeled data according to the intrinsic graph structure. Nonetheless, most label propagation solutions work under relatively small-scale data and fail to cope with many real applications, such as social network analysis, where graphs usually have millions of nodes. In this paper, we propose a novel algorithm named \algo to deal with large-scale data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to reduce memory overhead and accelerate the solving process. We also give a theoretical analysis on the necessity of the warm-start technique for label propagation. Experiments show that our algorithm can handle million-scale graphs in few seconds while achieving highly competitive performance with existing algorithms.

scholarly journals A Topology Visualization Early Warning Distribution Algorithm for Large-Scale Network Security Incidents

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Hui He ◽  
Guotao Fan ◽  
Jianwei Ye ◽  
Weizhe Zhang
Keyword(s):  
Network Security ◽  
Network Topology ◽  
Early Warning ◽  
Early Warning System ◽  
Large Scale ◽  
Warning System ◽  
Small Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Security Incidents

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

scholarly journals Impact of MRAI Timer on BGP Updates and Convergence Time

2018 ◽  
Vol 12 (2) ◽  
pp. 873 ◽  
Author(s):  
Rohit Nilkanth Devikar ◽  
Dipak V. Patil ◽  
V Chandra Prakash
Keyword(s):  
Routing Protocol ◽  
Large Scale ◽  
Autonomous Systems ◽  
Convergence Time ◽  
Huge Number ◽  
Internet Applications ◽  
Slow Convergence ◽  
Large Scale Network ◽  
Interval Timer ◽  
Scale Network

<p>BGP is a vital routing protocol for the communication amongst autonomous systems in the internet and has been broadly applied in all categories of large scale network. The inter-domain routing protocol (BGP) shows slow convergence, which effects on many internet applications due to its high convergence delay. The network operators broadly use different MRAI timers in BGP routers to deal with the issue of growing convergence time of the network. The variation in MRAI timer and its impact on network convergence and update messages has been broadly studied over the years. The increasing size of autonomous systems leads to rise in number of MRAI timers. Hence, the optimum use of MRAI timers can decrease the problem of slow convergence and necessity of huge number of MRAI timers. The proposed system uses the ckle minimum route advertisement interval timer (FMRAI) for fast update of routing table, which leads to reduce the convergence time of a network. In comparison with static MRAI timer of 30s the FMRAI timer leads to better result in terms of convergence time and number of update messages.</p>

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network
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