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Method for evaluating the importance of power grid nodes based on pagerank algorithm

IET GENERATION TRANSMISSION & DISTRIBUTION 2014年第11期8卷 1843-1847页

作者： Li, Canbing Liu, Wencan Cao, Yijia Chen, Hao Fang, Baling Zhang, Wei Shi, Haiqing Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China

Cascading failures can easily occur and cause a major blackout in power grid when an important node breaks down. It is an essential problem to evaluate the importance of nodes in power system planning and operation. In this study, a method for evaluating the importance of power grid nodes based on pagerank (PR) algorithm is proposed. First, according to the comparison of the internet and power grid topology, a directed graph is established. Second, based on the directed graph, an index is proposed to estimate the importance of power grid nodes based on PR algorithm. Then, according to the characteristics of power grid, a modified algorithm, which takes the importance of nodal load, nodal load capacity and network topology into account, is proposed. Finally, case study shows the necessity of considering the factors to evaluate the importance of power grid nodes and the effective of index and algorithm to identify critical nodes in this study.

关键词： power grids power system reliability Internet power engineering computing directed graphs power grid nodes pagerank algorithm cascading failures power system planning PR algorithm power grid topology directed graph network topology nodal load capacity Internet

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Critical Nodes Identification for Power Grid Based on Electrical Topology and Power Flow Distribution

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IEEE SYSTEMS JOURNAL 2023年第3期17卷 4874-4884页

作者： Fan, Bing Shu, Nan Li, Zewei Li, Fangxing North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China State Grid Hebei Elect Power Co Ltd Rongcheng Cty Power Supply Branch Rongcheng 071700 Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

The continuous development of the power industry has driven the increasing scale and complexity of power grids. There are critical nodes within these grids, the failure of which can easily lead to serious power system accidents. In order to effectively identify the critical nodes in a power grid, a critical nodes identification algorithm considering electrical topology and power flow distribution (ETPD-CNIA) is proposed. First, a node interaction model based on the line outage distribution factors is presented. Then, the model is introduced into the pagerank algorithm to calculate the node importance based on the electrical network topology. Finally, considering the node importance based on both the electrical network topology and the actual power flow distribution by an adaptive coefficient, the critical nodes are identified effectively. Simulations on the IEEE 39-bus system and the IEEE 118-bus system show that, compared with existing algorithms, the proposed ETPD-CNIA is more effective and accurate in identifying critical nodes in the power grid.

关键词： Critical nodes identification node importance pagerank algorithm power grid

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Ranking systemically important financial institutions of the Chinese financial system: evidence from the higher-order temporal network

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APPLIED ECONOMICS LETTERS 2025年第7期32卷 987-1001页

作者： Tian, Sihua Li, Shaofang Gu, Qinen Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Southeast Univ Sch Econ & Management Nanjing Peoples R China Southeast Univ Sch Econ & Management Nanjing 211189 Peoples R China

Identifying systemically important financial institutions (SIFIs) plays a key role in regulating systemic risk. We construct higher-order temporal causal networks based on daily stock return data and rank the systemic importance of 45 listed financial institutions. The constructed networks provide evidence that financial institutions become more interrelated during crisis periods, and capture the higher-order dynamic characteristics of interconnectedness among institutions. The ranking results show that not only the largest financial institutions, but also smaller, highly interconnected institutions are systemically important and need to be regulated. Our findings highlight the importance of considering higher-order interconnectedness among financial institutions when labelling SIFIs.

关键词： Granger causality test higher-order temporal network systemically important financial institutions pagerank algorithm

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Novel Data-Driven Dynamic Network Science Application in algorithmic Trading 48

Novel Data-Driven Dynamic Network Science Application in Alg...

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48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Ranathungage, Thimani Thavaneswaran, Aerambamoorthy Liang, You Thulasiram, Ruppa Paseka, Alex Univ Manitoba Dept Stat Winnipeg MB Canada Toronto Metropolitan Univ Dept Math Toronto ON Canada Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Manitoba Dept Accounting & Finance Winnipeg MB Canada

ISBN: (纸本)9798350376975;9798350376968

One of the recent developments in computational finance has been the rise in using graph-based approaches to analyse stock market dynamics systematically. In algo trading literature, stocks for pairs trading and multiple trading are commonly selected by using Engle Granger and Johansen co-integration tests. Identifying all pairs eligible for trading in large datasets, entails a significant computational burden. In this paper, price correlation-based dynamic networks and differenced price series-based correlation networks and their importance ranks are used to pre-select pairs for trading. algorithmic trading profits using commonly used co-integration methods are compared with the profits made by proposed correlation-based financial networks. Unlike the existing work, the novelty of the paper is that it uses data-driven correlation-based financial network approach to propose a stock selection method for pairs trading. In this paper, profit per transaction is used to compare the trading strategies. The superiority of the financial network stock selection method is discussed as well in some detail.

关键词： Dynamic Trading Strategies Financial Networks pagerank algorithm Co-integration

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CUDA Accelerated Graph algorithms for Key Class Detection 28

CUDA Accelerated Graph Algorithms for Key Class Detection

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28th International Conference on System Theory Control and Computing

作者： Chirila, Ciprian-Bogdan Sora, Ioana Univ Politehn Comp & Informat Technol Dept Timisoara Romania

ISBN: (纸本)9798350364309;9798350364293

Key classes are deemed as pivotal elements within a software system, serving as focal points for reengineering or documentation endeavors. The identification of these key classes holds significant importance in contemporary practices, with numerous studies dedicated to automating their detection based on representations within class graphs. Research indicates that employing algorithms such as Hyperlink-Introduced Topic Search (HITS) and pagerank (PR) yields optimal precision and recall performance in identifying key classes. However, the runtime execution of these algorithms becomes critical, particularly when operating on graphs with varied weights attributed to class relationships. To address the challenge of runtime execution, we explore parallel implementations of these algorithms utilizing CUDA, invoked from a Java application through JCuda. Specifically, we investigate two approaches: i) employing Java virtual threads and ii) utilizing CUDA threads within the context of the JCuda library. CUDA has fundamentally transformed how we harness GPU acceleration across diverse computational tasks, spanning parallel processing, deep learning, and highperformance computing. Our experiments are conducted on a data set comprising 14 Java projects. Our findings reveal that the hardware parallel CUDA threading model significantly accelerates attribute computation, achieving a runtime reduction of 95% to 97% compared to the virtual threading model.

关键词： CUDA JavaVirtual Threads Hits algorithm pagerank algorithm key classes detection

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A pagerank-Based WeChat User Impact Assessment algorithm

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Journal of New Media 2021年第2期3卷 53-62页

作者： Qiong Wang Yuewen Luo Hongliang Guo Peng Guo Jinghao Wei Tie Lin Hunan University of Finance and Economics ChangshaChina University Malaysia Sabah Kota KinabaluMalaysia

In recent years,the mobile Internet has developed rapidly,and the network social platform has emerged as the times require,and more people make friends,chat and share dynamics through the network social *** network social platform is the virtual embodiment of the social network,each user represents a node in the directed graph of the social *** the most popular online social platform in China,WeChat has developed rapidly in recent *** user groups,powerful mobile payment capabilities,and massive amounts of data have brought great influence to *** present,the research on WeChat network at home and abroad mainly focuses on communication and sociology,but the research from the angle of influence is ***,based on the basic principle of pagerank,this paper proposes an influence evaluation model WURank algorithm suitable for WeChat network *** algorithm takes into account the shortcomings of the traditional pagerank algorithm,and objectively evaluates the real-time influence of WeChat users from the perspective of WeChat user behavior(including:sharing,commenting,mentioning,collecting,likes)and time factors.

关键词： WeChat influence pagerank algorithm WURank algorithm

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BioMDSum: An Effective Hybrid Biomedical Multi-Document Summarization Method Based on pagerank and Longformer Encoder-Decoder

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IEEE ACCESS 2024年 12卷 188013-188031页

作者： Aftiss, Azzedine Lamsiyah, Salima El Alaoui, Said Ouatik Schommer, Christoph Ibn Tofail Univ Natl Sch Appl Sci Engn Sci Lab Kenitra 14000 Morocco Univ Luxembourg Fac Sci Technol & Med Dept Comp Sci L-4364 Esch Sur Alzette Luxembourg

Biomedical multi-document summarization (BioMDSum) involves automatically generating concise and informative summaries from collections of related biomedical documents. While extractive summarization methods have shown promise, they often produce incoherent summaries. Onethe other hand, fully abstractive methods yield coherent summaries but demand extensive training datasets and computational resources due to the typically lengthy nature of biomedical documents. Toeaddress these challenges, wepropose a hybrid summarization method that combines the strengths of both approaches. The proposed method consists of two main phases: (i) an extractive summarization phase that uses k-means clustering to group similar sentences based on their cosine similarity between embeddings generated by the sentence-BERT model, followed by the pagerank algorithm for sentence scoring and selection;and (ii) an abstractive summarization phase that fine-tunes a Longform Encoder-Decoder (LED) transformer model to generate a concise and coherent summary from the sentences selected during the extractive phase. We conducted several experiments on the standard biomedical multi-document summarization datasets Cochrane and MS<^>2. The results demonstrate that the proposed method is competitive and outperforms recent state-of-the-art systems based on ROUGE evaluation measures. Specifically, our model achieved ROUGE-1, ROUGE-2, ROUGE-L, BERTScore, and METEOR scores of 29.41%, 6.57%, 18.31%, 85.95%, and 22.15% on the Cochrane dataset, and 28.79%, 8.22%, 17.93%, 85.51%, and 25.17% on the MS<^>2 dataset, respectively. Furthermore, aneablation analysis shows that integrating extractive and abstractive phases in our hybrid summarization method enhances the overall performance of the proposed approach.

关键词： Biomedical multi-document summarization hybrid summarization K-means clustering sentence-BERT pagerank algorithm longformer encoder-decoder Biomedical multi-document summarization hybrid summarization K-means clustering sentence-BERT pagerank algorithm longformer encoder-decoder

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Java single vs. platform vs. virtual threads runtime performance assessment in the context of key class detection 18

Java single vs. platform vs. virtual threads runtime perform...

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18th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Chirila, Ciprian-Bogdan Sora, Ioana Univ Politehn Timisoara Comp & Informat Technol Dept Timisoara Romania

ISBN: (纸本)9798350329537;9798350329520

Key classes are considered the most important classes of a software system. They represent the starting point for reengineering or documentation processes. The detection of key classes is considered crucial in the state of the art, many studies are focused on automatic detection based on class graph system representation. Studies show that class attributes computed with algorithms like Hyperlink-Introduced Topic Search (HITS) and pagerank (PR) give the best precision and recall performance values in detecting key classes. The runtime execution of the two algorithms is critical when they run on graphs having different class relationship weights. To ameliorate the time execution problem we experiment with the two algorithm parallel implementations based on Java: i) single thread, ii) platform or operating system threads and iii) virtual threads. The experiments are fulfilled on a set of 14 Java projects. The results show that single thread implementations for project having a relative small number of classes, namely under 1,200, perform better than platform threads implementations. Conversely, virtual threads perform better than any single thread implementation. We conclude that virtual thread model speeds up the computation of attributes with a runtime decrease of 58.41% against the single thread model.

关键词： Java threads Java virtual threads Hits algorithm pagerank algorithm key classes detection

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The assessment of class relations importance using enumeration in a graph representation model for key classes detection based on pagerank 26

The assessment of class relations importance using enumerati...

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26th International Conference on System Theory, Control and Computing (ICSTCC)

作者： Chirila, Ciprian-Bogdan Sora, Ioana Univ Politehn Comp & Informat Technol Dept Timisoara Romania

ISBN: (数字)9781665467469

ISBN: (纸本)9781665467469

Key classes are the most important classes of an object-oriented software system. They are usually described in project documentations or executive summaries representing the backbone of the software system. Key classes are vital in software reengineering tasks especially in the context where poor or no documentation is maintained with the project. Thus, detecting key classes becomes an important issue. Several methods for key classes detection were reported: static code analysis, dynamic code analysis, machine learning. In a static code analysis approach the model of a weighted graph is used to represent the relations between classes like: inheritance, interface realization, field usage, return type usage etc. On the weighted graph model we run a link analysis algorithm to rank the classes in their order of importance, thus detecting the top 20 most important ones. We intend to assess the importance of class relations by generating all possible weights combinations of 0 and 1 for each class relation in the weighted graph construction. We experiment our approach on 14 analyzed systems of different sizes regarding the number of classes and from domains like: games, web servers, utilities, etc.

关键词： key classes reengineering object-oriented technology pagerank algorithm Recall

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Diffpagerank: an efficient differential pagerank approach in MapReduce

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JOURNAL OF SUPERCOMPUTING 2021年第1期77卷 188-211页

作者： Abadeh, Maryam Nooraei Mirzaie, Mansooreh Islamic Azad Univ Abadan Branch Dept Comp Engn Abadan Iran Golpayegan Univ Technol Fac Elect & Comp Engn Golpayegan Iran

Unstructured big data processing requires efficient computational styles to rapidly analyze continuously changing data. Incremental processing is a promising technique to update search results without re-computing the whole process. One of the most important mining algorithms is pagerank which necessitates incremental processing and iterative computations to keep the mining results up-to-date. In this paper, a light and efficient pagerank algorithm is proposed using differential dataflow in the MapReduce programming style named Diffpagerank. The innovation of the proposed approach is investigated from two points of view: (1) the updating state is dependent on a partial order of changes in Diffpagerank computing, while the changes are applied based on complete order sequences in the standard incremental computing;and (2) in Diffpagerank, a set of updates rebuilds each version, while in the incremental systems, every single update continually bring up-to-date the current version state. Diffpagerank is compared with two other implementations of pagerank including standard and incremental pagerank in MapReduce in terms of processing time, CPU utilization and update speed of data mining results. The results show that if the changes on the input data are large, the efficiency of the differential method will considerably increase in terms of time than the standard and incremental pagerank approaches up to 61% and 86%, respectively. Diffpagerank is also compared with two state-of-the-art incremental graph processing benchmarks on Orkut and Twitter datasets in terms of execution time and the number of updates. The experimental comparisons on benchmark datasets show that our method reduces the number of updates and total execution time by using the differential MapReduce approach.

关键词： pagerank algorithm Big data MapReduce Differential dataflow Incremental processing

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