We present an edge crossing minimization algorithm for hierarchical graphs based on genetic algorithms, and comparing it with some heuristic algorithms. The proposed algorithm is more efficient and has the following a...We present an edge crossing minimization algorithm for hierarchical graphs based on genetic algorithms, and comparing it with some heuristic algorithms. The proposed algorithm is more efficient and has the following advantages: the frame of the algorithms is unified, the method is simple, and its implementation and revision are easy.展开更多
Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based me...Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.展开更多
A duo hierarchical graph model for conflict resolution is developed to investigate market competition between Airbus and Boeing over aircraft sales in the Asia Pacific region. The duo hierarchical graph model, a signi...A duo hierarchical graph model for conflict resolution is developed to investigate market competition between Airbus and Boeing over aircraft sales in the Asia Pacific region. The duo hierarchical graph model, a significant extension of the graph model for conflict resolution methodology, contains two common decision makers, who take part in two related subconflicts, as well as local decision makers, who participate in only one subconflict. New stability definitions are proposed to describe forms of sanction unique to the hierarchical model. The interrelationships between stabilities in the overall graph model and in the two local models are investigated. Then the duo hierarchical graph model is applied to the competition between Airbus and Boeing in both the wide and narrow body markets in the Asia-Pacific region. The two types of Asian airlines have different operating strategies, so that the two markets constitute sub-competitions that can be modelled naturally using the duo hierarchical graph model. The stability results indicate a resolution for all decision makers that implies marketing strategies for the aircraft manufacturers and guidelines for aircraft purchase by the airlines. Thus, this model provides decision makers with a comprehensive understanding of the dynamics of the comoetition and guidance in identifving beneficial actions.展开更多
A novel two-level hierarchical graph model is developed to analyze international climate change negotiations with hierarchical structures:the negotiations take place between two nations and between each nation and its...A novel two-level hierarchical graph model is developed to analyze international climate change negotiations with hierarchical structures:the negotiations take place between two nations and between each nation and its provincial governments.The two national government are two decision makers at the top level.Within each nation,the two provincial governments negotiate with the national government at the lower level.The theoretical structure of this novel model,including decision makers,options,moves,and preference relations,are developed.The interrelationship between the stabilities in the two-level hierarchical graph model and the stabilities in local models are investigated by theorems.These theorems can be utilized to calculate complete stabilities in the two-level hierarchical graph model when the stabilities in local graph models are known.The international climate change negotiations as the illustrative example is then investigated in detail.The extra equilibrium,uniquely obtained by this novel methodology,suggests that opposition may still be from one provincial government when the national government does not sign the international climate agreement and implements existing environmental laws.Compared with other approaches,this novel methodology is an effective and flexible tool in analyzing hierarchical conflicts at two levels by providing decision makers with strategic resolutions with broader vision.展开更多
In edge-distributed environments,spatiotemporal graphs provide a promising solution for capturing the complex dependencies among nodes and edges necessary for accurate wind speed forecasting.These dependencies involve...In edge-distributed environments,spatiotemporal graphs provide a promising solution for capturing the complex dependencies among nodes and edges necessary for accurate wind speed forecasting.These dependencies involve spatial and temporal interactions that are crucial for modeling dynamic weather patterns.However,challenges,such as effectively maintaining spatial dependency information across spatiotemporal subgraphs,can lead to reduced prediction accuracy.Additionally,managing high communication costs,associated with the need for frequent and intensive data exchanges required for real-time forecasting across distributed nodes,poses significant hurdles.To address these issues,we propose graph coarsening-based cross-subgraph message passing with edge collaboration training mechanism(namely ComPact),a novel approach that simplifies graph structures through graph coarsening while preserving essential spatiotemporal dependencies.This coarsening process minimizes communication overhead and enables effective cross-subgraph message passing,capturing both local and long-range dependencies.ComPact further leverages hierarchical graph learning and structured edge collaboration to integrate global information into local subgraphs,enhancing predictive performance.Experimental validation on large-scale datasets,primarily the WindPower dataset,demonstrates ComPact’s superiority in wind speed forecasting,with up to a 31.82%reduction in Mean Absolute Error(MAE)and 11.8%lower in Mean Absolute Percentage Error(MAPE)compared to federated learning baselines.展开更多
Many cognitive studies have indicated that the path simplicity may be as important as its distance travelled.However,the optimality of paths for current navigation system is often judged purely on the distance travell...Many cognitive studies have indicated that the path simplicity may be as important as its distance travelled.However,the optimality of paths for current navigation system is often judged purely on the distance travelled or time cost,and not the path simplicity.To balance these factors,this paper presented an algorithm to compute a path that not only possesses fewest turns but also is as short as possible by utilizing the breadth-first-search strategy.The proposed algorithm started searching from a starting point,and expanded layer by layer through searching zero-level reachable points until the endpoint is found,and then deleted unnecessary points in the reverse direction.The forward searching and backward cleaning strategies were presented to build a hierarchical graph of zero-level reachable points,and form a fewestturn-path graph(G^(*)).After that,a classic Dijkstra shortest path algorithm was executed on the G^(*) to obtain a fewestturn-and-shortest path.Comparing with the shortest path in Baidu map,the algorithm in this work has less than half of the turns but the nearly same length.The proposed fewest-turn-and-shortest path algorithm is proved to be more suitable for human beings according to human cognition research.展开更多
Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the p...Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the poor efficiency of general link prediction methods applied to brain networks, this paper proposes a hierarchical random graph model based on maximum likelihood estimation. This algorithm uses brain network data to create a hierarchical random graph model. Then, it samples the space of all possible dendrograms using a Markov-chain Monte Carlo algorithm. Finally, it calculates the average connection probability. It also employs an evaluation index. Comparing link prediction in a brain network with link prediction in three different networks （Treponemapallidum metabolic network, terrorist networks, and grassland species food webs） using the hierarchical random graph model, experimental results show that the algorithm applied to the brain network has the highest prediction accuracy in terms of AUC scores. With the increase of network scale, AUC scores of the brain network reach 0.8 before gradually leveling off. In addition, the results show AUC scores of various algorithms computed in networks of eight different scales in 28 normal people. They show that the HRG algorithm is far better than random prediction and the ACT global index, and slightly inferior to local indexes CN and LP. Although the HRG algorithm does not produce the best results, its forecast effect is obvious, and shows good time complexity.展开更多
Despite the growing attention on blockchain,phishing activities have surged,particularly on newly established chains.Acknowledging the challenge of limited intelligence in the early stages of new chains,we propose ADA...Despite the growing attention on blockchain,phishing activities have surged,particularly on newly established chains.Acknowledging the challenge of limited intelligence in the early stages of new chains,we propose ADA-Spearan automatic phishing detection model utilizing adversarial domain adaptive learning which symbolizes the method’s ability to penetrate various heterogeneous blockchains for phishing detection.The model effectively identifies phishing behavior in new chains with limited reliable labels,addressing challenges such as significant distribution drift,low attribute overlap,and limited inter-chain connections.Our approach includes a subgraph construction strategy to align heterogeneous chains,a layered deep learning encoder capturing both temporal and spatial information,and integrated adversarial domain adaptive learning in end-to-end model training.Validation in Ethereum,Bitcoin,and EOSIO environments demonstrates ADA-Spear’s effectiveness,achieving an average F1 score of 77.41 on new chains after knowledge transfer,surpassing existing detection methods.展开更多
文摘We present an edge crossing minimization algorithm for hierarchical graphs based on genetic algorithms, and comparing it with some heuristic algorithms. The proposed algorithm is more efficient and has the following advantages: the frame of the algorithms is unified, the method is simple, and its implementation and revision are easy.
基金supported by Macao Science and Technology Development Fund,Macao SAR,China(Grant No.:0043/2023/AFJ)the National Natural Science Foundation of China(Grant No.:22173038)Macao Polytechnic University,Macao SAR,China(Grant No.:RP/FCA-01/2022).
文摘Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.
文摘A duo hierarchical graph model for conflict resolution is developed to investigate market competition between Airbus and Boeing over aircraft sales in the Asia Pacific region. The duo hierarchical graph model, a significant extension of the graph model for conflict resolution methodology, contains two common decision makers, who take part in two related subconflicts, as well as local decision makers, who participate in only one subconflict. New stability definitions are proposed to describe forms of sanction unique to the hierarchical model. The interrelationships between stabilities in the overall graph model and in the two local models are investigated. Then the duo hierarchical graph model is applied to the competition between Airbus and Boeing in both the wide and narrow body markets in the Asia-Pacific region. The two types of Asian airlines have different operating strategies, so that the two markets constitute sub-competitions that can be modelled naturally using the duo hierarchical graph model. The stability results indicate a resolution for all decision makers that implies marketing strategies for the aircraft manufacturers and guidelines for aircraft purchase by the airlines. Thus, this model provides decision makers with a comprehensive understanding of the dynamics of the comoetition and guidance in identifving beneficial actions.
基金The authors would like to thank the anonymous referees for carefully reading this paper and having provided meaningful suggestions which helped improve the quality of paper.This paper should be dedicated to Dr.Ye Chen who was a coauthor and passed away in June,2019.This research was supported by National Natural Science Foundation of China under Grant No.71601096,China Postdoctoral Science Foundation under Grant No.2019M661838,the Fundamental Research Funds for Central Universities(China)under Grant No.NS2020061,and the Natural Science Young Scholar Foundation of Jiangsu,China,under Grant No.BK20160809.
文摘A novel two-level hierarchical graph model is developed to analyze international climate change negotiations with hierarchical structures:the negotiations take place between two nations and between each nation and its provincial governments.The two national government are two decision makers at the top level.Within each nation,the two provincial governments negotiate with the national government at the lower level.The theoretical structure of this novel model,including decision makers,options,moves,and preference relations,are developed.The interrelationship between the stabilities in the two-level hierarchical graph model and the stabilities in local models are investigated by theorems.These theorems can be utilized to calculate complete stabilities in the two-level hierarchical graph model when the stabilities in local graph models are known.The international climate change negotiations as the illustrative example is then investigated in detail.The extra equilibrium,uniquely obtained by this novel methodology,suggests that opposition may still be from one provincial government when the national government does not sign the international climate agreement and implements existing environmental laws.Compared with other approaches,this novel methodology is an effective and flexible tool in analyzing hierarchical conflicts at two levels by providing decision makers with strategic resolutions with broader vision.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.Ltd.(No.J2023153).
文摘In edge-distributed environments,spatiotemporal graphs provide a promising solution for capturing the complex dependencies among nodes and edges necessary for accurate wind speed forecasting.These dependencies involve spatial and temporal interactions that are crucial for modeling dynamic weather patterns.However,challenges,such as effectively maintaining spatial dependency information across spatiotemporal subgraphs,can lead to reduced prediction accuracy.Additionally,managing high communication costs,associated with the need for frequent and intensive data exchanges required for real-time forecasting across distributed nodes,poses significant hurdles.To address these issues,we propose graph coarsening-based cross-subgraph message passing with edge collaboration training mechanism(namely ComPact),a novel approach that simplifies graph structures through graph coarsening while preserving essential spatiotemporal dependencies.This coarsening process minimizes communication overhead and enables effective cross-subgraph message passing,capturing both local and long-range dependencies.ComPact further leverages hierarchical graph learning and structured edge collaboration to integrate global information into local subgraphs,enhancing predictive performance.Experimental validation on large-scale datasets,primarily the WindPower dataset,demonstrates ComPact’s superiority in wind speed forecasting,with up to a 31.82%reduction in Mean Absolute Error(MAE)and 11.8%lower in Mean Absolute Percentage Error(MAPE)compared to federated learning baselines.
基金This research was supported by the National Natural Science Foundation of China(Nos.41471332 and 41101354)the National High Technology Research and Development Program of China(863 Program)(No.2013AA12A302)+1 种基金the Fundamental Research Funds for the Central Universities(No.ZYGX2011J077)the Fund of China Scholarship Council.
文摘Many cognitive studies have indicated that the path simplicity may be as important as its distance travelled.However,the optimality of paths for current navigation system is often judged purely on the distance travelled or time cost,and not the path simplicity.To balance these factors,this paper presented an algorithm to compute a path that not only possesses fewest turns but also is as short as possible by utilizing the breadth-first-search strategy.The proposed algorithm started searching from a starting point,and expanded layer by layer through searching zero-level reachable points until the endpoint is found,and then deleted unnecessary points in the reverse direction.The forward searching and backward cleaning strategies were presented to build a hierarchical graph of zero-level reachable points,and form a fewestturn-path graph(G^(*)).After that,a classic Dijkstra shortest path algorithm was executed on the G^(*) to obtain a fewestturn-and-shortest path.Comparing with the shortest path in Baidu map,the algorithm in this work has less than half of the turns but the nearly same length.The proposed fewest-turn-and-shortest path algorithm is proved to be more suitable for human beings according to human cognition research.
基金financially supported by the National Natural Science Foundation of China (Nos. 61170136, 61373101, 61472270, and 61402318)the Natural Science Foundation of Shanxi (No. 2014021022-5)+1 种基金the Special/Youth Foundation of Taiyuan University of Technology (No. 2012L014)Youth Team Fund of Taiyuan University of Technology (Nos. 2013T047 and 2013T048)
文摘Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the poor efficiency of general link prediction methods applied to brain networks, this paper proposes a hierarchical random graph model based on maximum likelihood estimation. This algorithm uses brain network data to create a hierarchical random graph model. Then, it samples the space of all possible dendrograms using a Markov-chain Monte Carlo algorithm. Finally, it calculates the average connection probability. It also employs an evaluation index. Comparing link prediction in a brain network with link prediction in three different networks （Treponemapallidum metabolic network, terrorist networks, and grassland species food webs） using the hierarchical random graph model, experimental results show that the algorithm applied to the brain network has the highest prediction accuracy in terms of AUC scores. With the increase of network scale, AUC scores of the brain network reach 0.8 before gradually leveling off. In addition, the results show AUC scores of various algorithms computed in networks of eight different scales in 28 normal people. They show that the HRG algorithm is far better than random prediction and the ACT global index, and slightly inferior to local indexes CN and LP. Although the HRG algorithm does not produce the best results, its forecast effect is obvious, and shows good time complexity.
基金supported by National Key Research and Development Program of China(Nos.2023YFC3306305,2021YFF0307203,2019QY1300)Foundation Strengthening Program Technical Area Fund(No.2021-JCJQJJ-0908)+4 种基金technological project funding of the State Grid Corporation of China(Contract Number:SG270000YXJS2311060)Youth Innovation Promotion Association CAS(No.2021156)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDC02040100)National Natural Science Foundation of China(No.61802404)supported by the Program of Key Laboratory of Network Assessment Technology,the Chinese Academy of Sciences,Program of Beijing Key Laboratory of Network Security and Protection Technology.
文摘Despite the growing attention on blockchain,phishing activities have surged,particularly on newly established chains.Acknowledging the challenge of limited intelligence in the early stages of new chains,we propose ADA-Spearan automatic phishing detection model utilizing adversarial domain adaptive learning which symbolizes the method’s ability to penetrate various heterogeneous blockchains for phishing detection.The model effectively identifies phishing behavior in new chains with limited reliable labels,addressing challenges such as significant distribution drift,low attribute overlap,and limited inter-chain connections.Our approach includes a subgraph construction strategy to align heterogeneous chains,a layered deep learning encoder capturing both temporal and spatial information,and integrated adversarial domain adaptive learning in end-to-end model training.Validation in Ethereum,Bitcoin,and EOSIO environments demonstrates ADA-Spear’s effectiveness,achieving an average F1 score of 77.41 on new chains after knowledge transfer,surpassing existing detection methods.
