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Analysis and Mitigation of Cascading Failures Using a Stochastic interaction graph With Eigen-Analysis

IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第2期40卷 1675-1685页

作者： Guo, Zhenping Su, Xiaowen Sun, Kai Park, Byungkwon Simunovic, Srdjan Univ Tennessee Dept EECS Knoxville TN 37916 USA Soongsil Univ Dept Elect Engn Seoul 06978 South Korea Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37830 USA

In studies on complex network systems using graph theory, eigen-analysis is typically performed on an undirected graph model of the network. However, when analyzing cascading failures in a power system, the interactions among failures suggest the need for a directed graph beyond the topology of the power system to model directions of failure propagation. To accurately quantify failure interactions for effective mitigation strategies, this paper proposes a stochastic interaction graph model and associated eigen-analysis. Different types of modes on failure propagations are defined and characterized by the eigenvalues of a stochastic interaction matrix, whose absolute values are unity, zero, or in between. Finding and interpreting these modes helps identify the probable patterns of failure propagation, either local or widespread, and the participating components based on eigenvectors. Then, by lowering the failure probabilities of critical components highly participating in a mode of widespread failures, cascading can be mitigated. The validity of the proposed stochastic interaction graph model, eigen-analysis and the resulting mitigation strategies is demonstrated using simulated cascading failure data on an NPCC 140-bus system.

关键词： Power system protection Power system faults Stochastic processes Power systems Data models Eigenvalues and eigenfunctions Directed graphs Cascading failure directed graph interaction graph eigen-analysis stochastic interaction model

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Knowledge interaction graph guided prompting for event causality identification

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APPLIED INTELLIGENCE 2025年第2期55卷 1-14页

作者： Hu, Ruijuan Li, Jian Liu, Haiyan Qi, Guilin Zhang, Yuxin PLA Strateg Support Force Informat Engn Univ Zhengzhou 450001 Henan Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 211100 Jiangsu Peoples R China

Event causality identification (ECI) aims to identify causality between event pairs in a text, and is commonly approached as a supervised classification task using pre-trained language models (PLMs). However, limitations in implicit causality identification and insufficient event-knowledge interaction pose significant challenges to ECI. To address these issues, we propose a novel Knowledge interaction graph guided Prompt Tuning (KIGP), which leverages prompt tuning and knowledge interaction to fully exploit the potential of PLMs for ECI by integrating external knowledge. Specifically, to accurately capture implicit causality, we design the guidance mechanism and construct event-knowledge interaction graphs that enable external knowledge to enhance event representations through deep interaction between events and knowledge. Experimental results on two benchmark datasets demonstrate that our model outperforms existing approaches significantly.

关键词： Event causality identification Prompt tuning Event knowledge interaction graph

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interaction graph-Based Active Islanding to Mitigate Cascading Outages

Interaction Graph-Based Active Islanding to Mitigate Cascadi...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Ju, Wenyun Sun, Kai Yao, Rui Univ Tennessee Knoxville TN 37996 USA

ISBN: (纸本)9781728119816

In order to mitigate the propagation of cascading outages, an online scheme of interaction graph-based active islanding is proposed. Active islanding will be performed if a critical component is involved in the outage propagation path. The failure of each of critical components can cause a large amount of load loss, and their sets are identified offline based on the interaction graph constructed from simulations or historical data. Test results on cascading outage scenarios of the NPCC power system validate the effectiveness of the proposed active islanding scheme in significantly reducing the amount of load loss compared with that without active islanding.

关键词： Terms-Cascading outages active islanding interaction graph critical component mitigation scheme

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On the influence of the interaction graph on a finite dynamical system

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NATURAL COMPUTING 2020年第1期19卷 15-28页

作者： Gadouleau, Maximilien Univ Durham Departement Comp Sci Durham England

A finite dynamical system (FDS) is a system of multivariate functions over a finite alphabet, that is typically used to model a network of interacting entities. The main feature of a finite dynamical system is its interaction graph, which indicates which local functions depend on which variables;the interaction graph is a qualitative representation of the interactions amongst entities on the network. As such, a major problem is to determine the effect of the interaction graph on the dynamics of the FDS. In this paper, we are interested in three main properties of an FDS: the number of images (the so-called rank), the number of periodic points (the so-called periodic rank) and the number of fixed points. In particular, we investigate the minimum, average, and maximum number of images (or periodic points, or fixed points) of FDSs with a prescribed interaction graph and a given alphabet size;thus yielding nine quantities to study. The paper is split into two parts. The first part considers the minimum rank, for which we derive the first meaningful results known so far. In particular, we show that the minimum rank decreases with the alphabet size, thus yielding the definition of an absolute minimum rank. We obtain lower and upper bounds on this absolute minimum rank, and we give classification results for graphs with very low (or highest) rank. The second part is a comprehensive survey of the results obtained on the nine quantities described above. We not only give a review of known results, but we also give a list of relevant open questions.

关键词： Finite dynamical systems Boolean networks interaction graph Fixed points Periodic points Rank

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An Enhanced Recommendation Model Based on Review Text graph and interaction graph

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IEEE ACCESS 2023年 11卷 88234-88244页

作者： Yang, Shuang Cai, Xuesong East China Normal Univ Shanghai Inst AI Educ Shanghai 200062 Peoples R China East China Normal Univ Sch Software Engn Shanghai 200062 Peoples R China Shanghai Synergy Digital Technol Innovat Inst Shanghai 200940 Peoples R China

User's review text data and rating data, as two major information sources of the recommender system, reflect user's preferences and item characteristics from two different perspectives. Many existing methods rely on one or the other data to make recommendations, ignoring the potential collaborative effects between the two. In terms of processing review data, the existing neural combination models mainly capture the local and continuous dependencies between adjacent words in the review text but have limited ability to capture the global and discontinuous dependencies. Therefore, we propose an enhanced recommendation model based on both the review text graph and the interaction graph. On the one hand, the model represents the review text of each user and item as a graph and uses the graph structure to capture the long-term, global, and discontinuous dependencies between words in the review text. A graph attention network based on connection relationships is used to aggregate the adjacency information of each node while taking word order relationships into account. On the other hand, the model builds an interaction graph based on user-item ratings for feature mining. The results of the two parts are combined to complete the prediction. We conduct experiments on three datasets and the results show that the proposed method can improve the recommendation performance.

关键词： Recommender system sentiment analysis review graph interaction graph

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Lexicon Knowledge Boosted interaction graph Network for Adverse Drug Reaction Recognition From Social Media

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第7期25卷 2777-2786页

作者： Li, Zhiheng Yang, Zhihao Wang, Lei Zhang, Yin Lin, Hongfei Wang, Jian Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Dalian Univ Technol Coll Comp Sci & Technol Dalian 116024 Peoples R China Beijing Inst Hlth Adm & Med Informat Beijing 100850 Peoples R China

The World Health Organization underlines the significance of adverse drug reaction (ADR) reports for patients' safety. Actually, many potential ADRs tend to be under-reported in post-market ADR surveillance. Recognizing ADRs from social media is indispensably important and could complement post-market ADR surveillance for more effective pharmacovigilance studies. However, previous approaches pose two challenges: 1) ADRs show high expression variability in social media, and thus, many potential ADRs are out-of-lexicon ones, which are difficult to be recognized, and 2) most phrasal ADRs are non-standard mentions and their boundaries are difficult to identify accurately. To tackle these challenges, we design three interaction graphs and propose a neural network approach, i.e., interaction graph Network (IGN). Specifically, to recognize more out-of-lexicon ADRs, besides the mentions in ADR lexicon, noun phrases in the input sentence are regarded as candidate phrases and their features are taken into considerations. Moreover, in an attempt to accurately identify ADR boundaries, three word-phrase interaction graphs are designed to represent lexicon knowledge and are encoded using graph attention networks (GATs) to directly integrate various boundary and contextual information of candidate phrases into ADR recognition. Experimental results on two benchmark datasets show that IGN can recognize ADR accurately and consistently outperforms other state-of-the-art approaches.

关键词： Social networking (online) Task analysis Text recognition Semantics Knowledge engineering Drugs Chemicals ADR recognition interaction graph graph attention network social media

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The interaction graph structure of mass-action reaction networks

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JOURNAL OF MATHEMATICAL BIOLOGY 2012年第2期65卷 375-402页

作者： Domijan, Mirela Pecou, Elisabeth Univ Warwick Warwick Syst Biol Ctr Coventry CV4 7AL W Midlands England Univ Nice Sophia Antipolis Parc Valrose Lab Jean Alexandre Dieudonne UMR 6621 CNRS F-06108 Nice 02 France

Behaviour of chemical networks that are described by systems of ordinary differential equations can be analysed via the associated graph structures. This paper deals with observations based on the interaction graph which is defined by the signs of the Jacobian matrix entries. Some of the important graph structures linked to network dynamics are signed circuits and the nucleus (or Hamiltonian hooping). We use mass-action chemical reaction networks as an example to showcase interesting observations about the aforementioned interaction graph structures. We show that positive circuits and specific nucleus structures (associated to multistationarity) are always present in a great generic class of mass-action chemical and biological networks. The theory of negative circuits remains poorly understood, but there is some evidence that they are indicators of stable periodicity. Here we introduce the concept of non-isolated circuits which indicate the presence of a negative circuit.

关键词： Multistationarity Oscillations Feedback circuits interaction graph Jacobian matrix

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CoPE: Modeling Continuous Propagation and Evolution on interaction graph 21

CoPE: Modeling Continuous Propagation and Evolution on Inter...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zhang, Yao Xiong, Yun Li, Dongsheng Shan, Caihua Ren, Kan Zhu, Yangyong Fudan Univ Shanghai Key Lab Data Sci Sch Comp Shanghai Peoples R China Fudan Univ Shanghai Inst Adv Commun & Data Sci Shanghai Key Lab Data Sci Sch Comp Sci Shanghai Peoples R China Microsoft Res Asia Shanghai Peoples R China

ISBN: (纸本)9781450384469

Human interactions with items are being constantly logged, which enables advanced representation learning and facilitates various tasks. Instead of generating static embeddings at the end of training, several temporal embedding methods were recently proposed to learn user and item embeddings as functions of time, where each entity has a trajectory of embedding vectors aiming to encode the full dynamics. However, these methods may not be optimal to encode the dynamical behaviors on the interaction graphs in that they can not generate "fully"-temporal embeddings and do not consider information propagation. In this paper, we tackle the issues and propose CoPE (Continuous Propagation and Evolution). We use an ordinary differential equation based graph neural network to model information propagation and more sophisticated evolution patterns. We train CoPE on sequences of interactions with the help of meta-learning to ensure fast adaptation to the most recent interactions. We evaluate CoPE on three tasks and prove its effectiveness.

关键词： interaction graph temporal embedding graph neural networks

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News Feeds Optimization via Users' interaction graph over Mobile Social Networks

News Feeds Optimization via Users' Interaction Graph over Mo...

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International Conference on Cloud Computing and Big Data (CLOUDCOM-ASIA)

作者： Huang, Jinzhou Jin, Hai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan 430074 Peoples R China

ISBN: (纸本)9781479928293

Online social networks are increasingly playing roles of propagating news. We are surrounded with diverse data every day in today's information exploding era and suffer from the littery contents. Furthermore, due to most users of online social networks have turned their platforms from PCs to smart phones, it is a challenge to select contents that users interested in with resource constraints in mobile social networks. Considering the contradiction between large volume of data and memory constraints in mobile social networks, we present an efficient news filtering mechanism via users' interaction to avoid unnecessary display of data that barely read. By leveraging the Bloom filter technology, we can record users' interaction dynamically with a slight false positive, and filter the little possible readable updates of friends from their News Feed sites. We conduct a simulation to evaluate our design. Results show significant performance for storage saving and high filtering rate.

关键词： News Feeds interaction graph mobile social network Bloom filter

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Analysis and Mitigation of Cascading Outages Using an interaction graph Addressing Transient Stability

Analysis and Mitigation of Cascading Outages Using an Intera...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Guo, Zhenping Sue, Xiaowen Sun, Kai Simunovic, Srdjan Univ Tennessee Dept EECS Knoxville TN 37996 USA Univ Texas San Antonio Dept ECE San Antonio TX 78249 USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN USA

ISBN: (纸本)9798350381849;9798350381832

Cascading outages of power systems pose great threats to system security and reliability, potentially leading to large-scale blackouts. For analysis and mitigation of cascading outages, this paper proposes a transient stability-incorporated interaction graph. This graph statistically quantifies the interactions among line outages and instabilities of generators, which can model propagation paths and patterns of cascading outages. Compared with an interaction graph that only models line outages, this new interaction graph provides important insights on how transient instability occurs along with cascading outages. It also offers more effective strategies for mitigating outage propagation. The proposed interaction graph can be constructed from datasets of historical or simulated cascading events. It is demonstrated on an NPCC 140-bus system with mitigation strategies.

关键词： Cascading outages transient stability interaction graph mitigation

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