Relevant bibliographies by topics / Temporal Graph Processing

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Temporal Graph Processing'

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Published: 6 September 2023

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Journal articles on the topic "Temporal Graph Processing"

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Christensen, Andrew J., Ananya Sen Gupta, and Ivars Kirsteins. "Sonar target feature representation using temporal graph networks." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A102. http://dx.doi.org/10.1121/10.0010791.

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Autonomous sonar target recognition suffers from uncertainty caused by waveguide distortions to signal, unknown target geometry, and morphing target features. Typical “black-box” neural networks do not produce physically interpretable features and, therefore, are not effective in meeting these challenges. The primary objective of our work is to harness signal processing with machine learning to extract braided features that allow such physical interpretation by a domain expert. In this work, we introduce a feature extraction method using graph neural networks (GNNs) that seeks to discover braid manifolds from sonar magnitude spectra data. The approach involves representing the sonar magnitude spectra as sparse, dynamic graphs. These dynamic graphs can then be fed into a GNN as sequences of timed events to produce feature dictionaries that are resilient to environmental uncertainty and agnostic to ping direction. The ability of GNNs to learn complex systems of interactions makes them a great choice for braid-like feature discovery. To handle the evolving dynamic features of the sonar spectra graphs, a variation of a GNN, called temporal graph networks (TGNs), is used. TGNs utilize memory modules and graph-based operators to outperform previous GNN-based approaches when handling dynamic graphs. We use TGNs to model the evolution of the sonar spectra graphs and ultimately perform graph-based classification. Preliminary results performed on the Malta Plateau field experiment are presented.
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Choi, Jeongwhan, Hwangyong Choi, Jeehyun Hwang, and Noseong Park. "Graph Neural Controlled Differential Equations for Traffic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6367–74. http://dx.doi.org/10.1609/aaai.v36i6.20587.

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Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has been fierce competition and many novel methods have been proposed. In this paper, we present the method of spatio-temporal graph neural controlled differential equation (STG-NCDE). Neural controlled differential equations (NCDEs) are a breakthrough concept for processing sequential data. We extend the concept and design two NCDEs: one for the temporal processing and the other for the spatial processing. After that, we combine them into a single framework. We conduct experiments with 6 benchmark datasets and 20 baselines. STG-NCDE shows the best accuracy in all cases, outperforming all those 20 baselines by non-trivial margins.
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Zhao, Xiaojuan, Aiping Li, Rong Jiang, Kai Chen, and Zhichao Peng. "Householder Transformation-Based Temporal Knowledge Graph Reasoning." Electronics 12, no. 9 (April 26, 2023): 2001. http://dx.doi.org/10.3390/electronics12092001.

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Knowledge graphs’ reasoning is of great significance for the further development of artificial intelligence and information retrieval, especially for reasoning over temporal knowledge graphs. The rotation-based method has been shown to be effective at modeling entities and relations on a knowledge graph. However, due to the lack of temporal information representation capability, existing approaches can only model partial relational patterns and they cannot handle temporal combination reasoning. In this regard, we propose HTTR: Householder Transformation-based Temporal knowledge graph Reasoning, which focuses on the characteristics of relations that evolve over time. HTTR first fuses the relation and temporal information in the knowledge graph, then uses the Householder transformation to obtain an orthogonal matrix about the fused information, and finally defines the orthogonal matrix as the rotation of the head-entity to the tail-entity and calculates the similarity between the rotated vector and the vector representation of the tail entity. In addition, we compare three methods for fusing relational and temporal information. We allow other fusion methods to replace the current one as long as the dimensionality satisfies the requirements. We show that HTTR is able to outperform state-of-the-art methods in temporal knowledge graph reasoning tasks and has the ability to learn and infer all of the four relational patterns over time: symmetric reasoning, antisymmetric reasoning, inversion reasoning, and temporal combination reasoning.
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Liu, Jun. "Motion Action Analysis at Basketball Sports Scene Based on Image Processing." Scientific Programming 2022 (March 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/7349548.

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To solve the problems of low accuracy and high time cost in manual recording and statistics of basketball data, an automatic analysis method of motion action under the basketball sports scene based on the spatial temporal graph convolutional neural network is proposed. By using the graph structure in the data structure to model the joints and limbs of the human body, and using the spatial temporal graph structure to model the posture action, the extraction and estimation of human body posture in basketball sports scenes are realized. Then, training combined with transfer learning, the recognition of motion fuzzy posture is realized through the classification and application of a label subset. Finally, using the self-made OpenCV to collect and calibrate NBA basketball videos, the effectiveness of the proposed method is verified by analyzing the motion action. The results show that the proposed method based on the spatial temporal graph convolutional neural network can recognize all kinds of movements in different basketball scenes. The average recognition accuracy is more than 75%. It can be seen that the method has certain practical application value. Compared with the common motion analysis method feature descriptors, the motion action analysis method based on the spatial temporal graph convolution neural network has higher identification accuracy and can be used for motion action analysis in the actual basketball sports scenes.
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Li, Jing, Wenyue Guo, Haiyan Liu, Xin Chen, Anzhu Yu, and Jia Li. "Predicting User Activity Intensity Using Geographic Interactions Based on Social Media Check-In Data." ISPRS International Journal of Geo-Information 10, no. 8 (August 17, 2021): 555. http://dx.doi.org/10.3390/ijgi10080555.

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Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we propose a deep learning model, the geographical interactions-weighted graph convolutional network-gated recurrent unit (GGCN-GRU), which is good at fitting nonlinear spatio-temporal relationships and incorporates users’ geographic interactions to construct spatial relationships in the form of graphs as the input. The model consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). The GCN, which is efficient at processing graphs, extracts spatial features. These features are then input into the GRU, which extracts their temporal features. Finally, the GRU output is passed through a fully connected layer to obtain the predictions. We validated this model using a social media check-in dataset and found that the geographical interactions graph construction method performs better than the baselines. This indicates that our model is appropriate for fitting the complex nonlinear spatio-temporal relationships that characterize user mobility and helps improve prediction accuracy when considering geographic flows.
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Ke, Xiangyu, Arijit Khan, and Francesco Bonchi. "Multi-relation Graph Summarization." ACM Transactions on Knowledge Discovery from Data 16, no. 5 (October 31, 2022): 1–30. http://dx.doi.org/10.1145/3494561.

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Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In this article, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive model of real-world activities, in which a relation can be a topic in social networks, an interaction type in genetic networks, or a snapshot in temporal graphs. The first approach that we consider for multi-relation graph summarization is a two-step method based on summarizing each relation in isolation, and then aggregating the resulting summaries in some clever way to produce a final unique summary. In doing this, as a side contribution, we provide the first polynomial-time approximation algorithm based on the k -Median clustering for the classic problem of lossless single-relation graph summarization. Then, we demonstrate the shortcomings of these two-step methods, and propose holistic approaches, both approximate and heuristic algorithms, to compute a summary directly for multi-relation graphs. In particular, we prove that the approximation bound of k -Median clustering for the single relation solution can be maintained in a multi-relation graph with proper aggregation operation over adjacency matrices corresponding to its multiple relations. Experimental results and case studies (on co-authorship networks and brain networks) validate the effectiveness and efficiency of the proposed algorithms.
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Zhang, Guoxing, Haixiao Wang, and Yuanpu Yin. "Multi-type Parameter Prediction of Traffic Flow Based on Time-space Attention Graph Convolutional Network." International Journal of Circuits, Systems and Signal Processing 15 (August 11, 2021): 902–12. http://dx.doi.org/10.46300/9106.2021.15.97.

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Graph Convolutional Neural Networks are more and more widely used in traffic flow parameter prediction tasks by virtue of their excellent non-Euclidean spatial feature extraction capabilities. However, most graph convolutional neural networks are only used to predict one type of traffic flow parameter. This means that the proposed graph convolutional neural network may only be effective for specific parameters of specific travel modes. In order to improve the universality of graph convolutional neural networks. By embedding time feature and spatio-temporal attention layer, we propose a spatio-temporal attention graph convolutional neural network based on the attention mechanism of the neural network. Through experiments on passenger flow data and vehicle speed data of two different travel modes (Hangzhou Metro Data and California Highway Data), it is verified that the proposed spatio-temporal attention graph convolutional neural network can be used to predict passenger flow and vehicle speed simultaneously. Meanwhile, the error distribution range of the proposed model is minimum, and the overall level of prediction results is more accurate.
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Zheng, Xiaolong, Dongdong Guan, Bangjie Li, Zhengsheng Chen, and Lefei Pan. "Global and Local Graph-Based Difference Image Enhancement for Change Detection." Remote Sensing 15, no. 5 (February 21, 2023): 1194. http://dx.doi.org/10.3390/rs15051194.

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Change detection (CD) is an important research topic in remote sensing, which has been applied in many fields. In the paper, we focus on the post-processing of difference images (DIs), i.e., how to further improve the quality of a DI after the initial DI is obtained. The importance of DIs for CD problems cannot be overstated, however few methods have been investigated so far for re-processing DIs after their acquisition. In order to improve the DI quality, we propose a global and local graph-based DI-enhancement method (GLGDE) specifically for CD problems; this is a plug-and-play method that can be applied to both homogeneous and heterogeneous CD. GLGDE first segments the multi-temporal images and DIs into superpixels with the same boundaries and then constructs two graphs for the DI with superpixels as vertices: one is the global feature graph that characterizes the association between the similarity relationships of connected vertices in the multi-temporal images and their changing states in a DI, the other is the local spatial graph that exploits the change information and contextual information of the DI. Based on these two graphs, a DI-enhancement model is built, which constrains the enhanced DI to be smooth on both graphs. Therefore, the proposed GLGDE can not only smooth the DI but also correct the it. By solving the minimization model, we can obtain an improved DI. The experimental results and comparisons on different CD tasks with six real datasets demonstrate the effectiveness of the proposed method.
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Steinbauer, Matthias, and Gabriele Anderst Kotsis. "DynamoGraph: extending the Pregel paradigm for large-scale temporal graph processing." International Journal of Grid and Utility Computing 7, no. 2 (2016): 141. http://dx.doi.org/10.1504/ijguc.2016.077491.

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Chen, Yaosen, Bing Guo, Yan Shen, Wei Wang, Weichen Lu, and Xinhua Suo. "Boundary graph convolutional network for temporal action detection." Image and Vision Computing 109 (May 2021): 104144. http://dx.doi.org/10.1016/j.imavis.2021.104144.

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Dissertations / Theses on the topic "Temporal Graph Processing"

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Kumar, Rohit 1986. "Temporal graph mining and distributed processing." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/620623.

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With the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamics of the network. Understanding the dynamics of the network is very important because it encapsulates the way we communicate, socialize, consume information and get influenced. To this end, in this PhD thesis, we focus on analyzing an interaction network to understand how the underlying network is being used. We define interaction network as a sequence of time-stamped interactions E over edges of a static graph G=(V, E). Interaction networks can be used to model many real-world networks for example, in a social network or a communication network, each interaction over an edge represents an interaction between two users, e.g., emailing, making a call, re-tweeting, or in case of the financial network an interaction between two accounts to represent a transaction. We analyze interaction network under two settings. In the first setting, we study interaction network under a sliding window model. We assume a node could pass information to other nodes if they are connected to them using edges present in a time window. In this model, we study how the importance or centrality of a node evolves over time. In the second setting, we put additional constraints on how information flows between nodes. We assume a node could pass information to other nodes only if there is a temporal path between them. To restrict the length of the temporal paths we consider a time window in this approach as well. We apply this model to solve the time-constrained influence maximization problem. By analyzing the interaction network data under our model we find the top-k most influential nodes. We test our model both on human-human interaction using social network data as well as on location-location interaction using location-based social network(LBSNs) data. In the same setting, we also mine temporal cyclic paths to understand the communication patterns in a network. Temporal cycles have many applications and appear naturally in communication networks where one person posts a message and after a while reacts to a thread of reactions from peers on the post. In financial networks, on the other hand, the presence of a temporal cycle could be indicative of certain types of fraud. We provide efficient algorithms for all our analysis and test their efficiency and effectiveness on real-world data. Finally, given that many of the algorithms we study have huge computational demands, we also studied distributed graph processing algorithms. An important aspect of distributed graph processing is to correctly partition the graph data between different machine. A lot of research has been done on efficient graph partitioning strategies but there is no one good partitioning strategy for all kind of graphs and algorithms. Choosing the best partitioning strategy is nontrivial and is mostly a trial and error exercise. To address this problem we provide a cost model based approach to give a better understanding of how a given partitioning strategy is performing for a given graph and algorithm.
Con el reciente crecimiento de las redes sociales y el deseo humano de interactuar con el mundo digital, una gran cantidad de datos de interacción humano-a-humano o humano-a-dispositivo se generan cada segundo. Con el auge de los dispositivos IoT, las interacciones dispositivo-a-dispositivo también están en alza. Todas estas interacciones no son más que una representación de como la red subyacente conecta distintas entidades en el tiempo. Modelar estas interacciones en forma de red de interacciones presenta una gran cantidad de oportunidades únicas para descubrir patrones interesantes y entender la dinamicidad de la red. Entender la dinamicidad de la red es clave ya que encapsula la forma en la que nos comunicamos, socializamos, consumimos información y somos influenciados. Para ello, en esta tesis doctoral, nos centramos en analizar una red de interacciones para entender como la red subyacente es usada. Definimos una red de interacciones como una sequencia de interacciones grabadas en el tiempo E sobre aristas de un grafo estático G=(V, E). Las redes de interacción se pueden usar para modelar gran cantidad de aplicaciones reales, por ejemplo en una red social o de comunicaciones cada interacción sobre una arista representa una interacción entre dos usuarios (correo electrónico, llamada, retweet), o en el caso de una red financiera una interacción entre dos cuentas para representar una transacción. Analizamos las redes de interacción bajo múltiples escenarios. En el primero, estudiamos las redes de interacción bajo un modelo de ventana deslizante. Asumimos que un nodo puede mandar información a otros nodos si estan conectados utilizando aristas presentes en una ventana temporal. En este modelo, estudiamos como la importancia o centralidad de un nodo evoluciona en el tiempo. En el segundo escenario añadimos restricciones adicionales respecto como la información fluye entre nodos. Asumimos que un nodo puede mandar información a otros nodos solo si existe un camino temporal entre ellos. Para restringir la longitud de los caminos temporales también asumimos una ventana temporal. Aplicamos este modelo para resolver este problema de maximización de influencia restringido temporalmente. Analizando los datos de la red de interacción bajo nuestro modelo intentamos descubrir los k nodos más influyentes. Examinamos nuestro modelo en interacciones humano-a-humano, usando datos de redes sociales, como en ubicación-a-ubicación usando datos de redes sociales basades en localización (LBSNs). En el mismo escenario también minamos camínos cíclicos temporales para entender los patrones de comunicación en una red. Existen múltiples aplicaciones para cíclos temporales y aparecen naturalmente en redes de comunicación donde una persona envía un mensaje y después de un tiempo reacciona a una cadena de reacciones de compañeros en el mensaje. En redes financieras, por otro lado, la presencia de un ciclo temporal puede indicar ciertos tipos de fraude. Proponemos algoritmos eficientes para todos nuestros análisis y evaluamos su eficiencia y efectividad en datos reales. Finalmente, dado que muchos de los algoritmos estudiados tienen una gran demanda computacional, también estudiamos los algoritmos de procesado distribuido de grafos. Un aspecto importante de procesado distribuido de grafos es el de correctamente particionar los datos del grafo entre distintas máquinas. Gran cantidad de investigación se ha realizado en estrategias para particionar eficientemente un grafo, pero no existe un particionamento bueno para todos los tipos de grafos y algoritmos. Escoger la mejor estrategia de partición no es trivial y es mayoritariamente un ejercicio de prueba y error. Con tal de abordar este problema, proporcionamos un modelo de costes para dar un mejor entendimiento en como una estrategia de particionamiento actúa dado un grafo y un algoritmo.
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Kumar, Rohit. "Temporal Graph Mining and Distributed Processing." Doctoral thesis, Universite Libre de Bruxelles, 2018. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/271527.

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With the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamics of the network. Understanding the dynamics of the network is very important because it encapsulates the way we communicate, socialize, consume information and get influenced. To this end, in this PhD thesis, we focus on analyzing an interaction network to understand how the underlying network is being used. We define interaction network as a sequence of time-stamped interactions E over edges of a static graph G=(V, E). Interaction networks can be used to model many real-world networks for example, in a social network or a communication network, each interaction over an edge represents an interaction between two users, e.g. emailing, making a call, re-tweeting, or in case of the financial network an interaction between two accounts to represent a transaction.We analyze interaction network under two settings. In the first setting, we study interaction network under a sliding window model. We assume a node could pass information to other nodes if they are connected to them using edges present in a time window. In this model, we study how the importance or centrality of a node evolves over time. In the second setting, we put additional constraints on how information flows between nodes. We assume a node could pass information to other nodes only if there is a temporal path between them. To restrict the length of the temporal paths we consider a time window in this approach as well. We apply this model to solve the time-constrained influence maximization problem. By analyzing the interaction network data under our model we find the top-k most influential nodes. We test our model both on human-human interaction using social network data as well as on location-location interaction using location-based social network(LBSNs) data. In the same setting, we also mine temporal cyclic paths to understand the communication patterns in a network. Temporal cycles have many applications and appear naturally in communication networks where one person posts a message and after a while reacts to a thread of reactions from peers on the post. In financial networks, on the other hand, the presence of a temporal cycle could be indicative of certain types of fraud. We provide efficient algorithms for all our analysis and test their efficiency and effectiveness on real-world data.Finally, given that many of the algorithms we study have huge computational demands, we also studied distributed graph processing algorithms. An important aspect of these algorithms is to correctly partition the graph data between different machines. A lot of research has been done on efficient graph partitioning strategies but there is no one good partitioning strategy for all kind of graphs and algorithms. Choosing the best partitioning strategy is nontrivial and is mostly a trial and error exercise. To address this problem we provide a cost model based approach to give a better understanding of how a given partitioning strategy is performing for a given graph and algorithm.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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Zhang, Hui. "Temporal subtraction of chest radiograph using graph cuts and free-form deformations." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/b40203451.

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Zhang, Hui, and 張暉. "Temporal subtraction of chest radiograph using graph cuts and free-form deformations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B40203451.

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Bautista, Ruiz Esteban. "Laplacian Powers for Graph-Based Semi-Supervised Learning." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEN081.

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Les techniques d’apprentissage semi-supervisé basées sur des graphes (G-SSL) permettent d’exploiter des données étiquetées et non étiquetées pour construire de meilleurs classifiers. Malgré de nombreuses réussites, leur performances peuvent encore être améliorées, en particulier dans des situations ou` les graphes ont une faible séparabilité de classes ou quand le nombres de sujets supervisés par l’expert est déséquilibrés. Pour aborder ces limitations on introduit une nouvelle méthode pour G-SSL, appel´ee Lγ -PageRank, qui constitue la principal contribution de cette th`ese. Il s’agit d’une g´en´eralisation de l’algorithme PageRank ´a partir de l’utilisation de puissances positives γ de la matrice Laplacienne du graphe. L’étude théorique de Lγ -PageRank montre que (i) pour γ < 1, cela correspond `a une extension de l’algorithme PageRank aux processus de vol de L´evy: ou` les marcheurs aléatoires peuvent désormais relier, en un seul saut, des nœuds distants du graphe; et (ii) pour γ > 1, la classification est effectué sur des graphes signés: ou` les nœuds appartenant `a une même classe ont plus de chances de partager des liens positifs, tandis que les nœuds de classes différentes ont plus de chances d’être connectés avec des arêtes négatifs. Nous montrons l’existence d’une puissance optimale γ qui maximise la performance de classification, pour laquelle une méthode d’estimation automatique est conçue et évaluée. Des expériences sur plusieurs jeux de données montrent que les marcheurs aléatoires de vols de Lévy peuvent améliorer la détection des classes ayant des structures locales complexes, tandis que les graphes signés permet d’améliorer considérablement la séparabilité des données et de surpasser le problème des données étiquetées non équilibrées. Dans un second temps, nous étudions des implémentations efficaces de Lγ -PageRank. Nous proposons des extensions de Power Iteration et Gauss-Southwell pour Lγ -PageRank, qui sont des algorithmes initialement conçues pour calculer efficacement la solution de la méthode PageRank standard. Ensuite, les versions dynamiques de ces algorithmes sont également étendues à Lγ -PageRank, permettant de mettre `a jour la solution de Lγ -PageRank en complexité sub-linéaire lorsque le graphe évolue ou que de nouvelles données arrivent. Pour terminer, nous appliquons Lγ -PageRank dans le contexte du routage Internet. Nous abordons le problème de l’identification des systèmes autonomes (AS) pour des arêtes inter-AS `a partir du réseau d’adresses IP et des registres publics des AS. Des expériences sur des mesures traceroute d’Internet montrent que Lγ -PageRank peut résoudre cette tâche sans erreurs, même lorsqu’il n’y a pas d’exemples étiquetés par l’expert pour la totalité des classes
Graph-Based Semi-Supervised Learning (G-SSL) techniques learn from both labelled and unla- belled data to build better classifiers. Despite successes, its performance can still be improved, particularly in cases of graphs with unclear clusters or unbalanced labelled datasets. To ad- dress such limitations, the main contribution of this dissertation is a novel method for G-SSL referred to as the Lγ -PageRank method. It consists of a generalization of the PageRank algo- rithm based on the positive γ-th powers of the graph Laplacian matrix. The theoretical study of Lγ -PageRank shows that (i) for γ < 1, it corresponds to an extension of the PageRank algo- rithm to L´evy processes: where random walkers can now perform far-distant jumps in a single step; and (ii) for γ > 1, it operates on signed graphs: where nodes belonging to one same class are more likely to share positive edges while nodes from different classes are more likely to be connected with negative edges. We show the existence of an optimal γ-th power that maximizes performance, for which a method for its automatic estimation is devised and assessed. Exper- iments on several datasets demonstrate that the L´evy flight random walkers can enhance the detection of classes with complex local structures and that the signed graphs can significantly improve the separability of data and also override the issue of unbalanced labelled data. In addition, we study efficient implementations of Lγ -PageRank. Extensions of Power Iteration and Gauss-Southwell, successful algorithms to efficiently compute the solution of the standard PageRank algorithm, are derived for Lγ -PageRank. Moreover, the dynamic versions of Power Iteration and Gauss-Southwell, which can update the solution of standard PageRank in sub- linear complexity when the graph evolves or new data arrive, are also extended to Lγ -PageRank. Lastly, we apply Lγ -PageRank in the context of Internet routing. We address the problem of identifying the Autonomous Systems (AS) of inter-AS links from the network of IP addresses and AS public registers. Experiments on tracerout measurements collected from the Internet show that Lγ -PageRank can solve this inference task with no errors, even when the expert does not provide labelled examples of all classes
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Chao, Tian-Jy. "Estimating temporary file sizes for query graphs in distributed relational database systems." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/90921.

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This thesis implements a part of the front-end software, the Optimizer, of the distributed database system being developed at Virginia Tech. The Optimizer generates a strategy for optimal query processing, and it presents and analyzes a given query by means of query trees and query graphs. This thesis develops PASCAL procedures that implement quantitative and qualitative rules to select query graphs requiring minimum communication costs. To develop the rules, the size of the temporary files generated after performing each required operation is estimated. The focus of this work is on the implementation of a new technique for estimating the temporary file sizes. Detailed discussion of this implementation is presented and illustrated with a complete example, followed by a comparison with one of the existing methods proposed by Dwyer.
M.S.
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Rajaei, Hoda. "Brain Connectivity Networks for the Study of Nonlinear Dynamics and Phase Synchrony in Epilepsy." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3882.

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Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy. A nonlinear recurrence-based method is applied to scalp EEG recordings to obtain connectivity maps using phase synchronization attributes. The pairwise connectivity measure is obtained from time domain data without any conversion to the frequency domain. The phase coupling value, which indicates the broadband interdependence of input data, is utilized for the graph theory interpretation of local and global assessment of connectivity activities. The method is applied to the population of pediatric individuals to delineate the epileptic cases from normal controls. A probabilistic approach proved a significant difference between the two groups by successfully separating the individuals with an accuracy of 92.8%. The investigation of connectivity patterns of the interictal epileptic discharges (IED), which were originated from focal and generalized seizures, was resulted in a significant difference ( ) in connectivity matrices. It was observed that the functional connectivity maps of focal IED showed local activities while generalized cases showed global activated areas. The investigation of connectivity maps that resulted from temporal lobe epilepsy individuals has shown the temporal and frontal areas as the most affected regions. In general, functional connectivity measures are considered higher order attributes that helped the delineation of epileptic individuals in the classification process. The functional connectivity patterns of interictal activities can hence serve as indicators of the seizure type and also specify the irritated regions in focal epilepsy. These findings can indeed enhance the diagnosis process in context to the type of epilepsy and effects of relative location of the 3D source of seizure onset on other brain areas.
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Hamon, Ronan. "Analyse de réseaux temporels par des méthodes de traitement du signal : application au système de vélos en libre-service à Lyon." Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1017/document.

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Les systèmes de vélos en libre-service sont devenus des éléments indispensables dans les offres de transport urbain des grandes villes mondiales. À partir des données que ces systèmes génèrent, il est possible d'avoir une caractérisation fine de l'utilisation du vélo en milieu urbain, tant sur des problématiques traitant du domaine des transports que des aspects socio-économiques. Comme pour de nombreux domaines profitant de la récente abondance en données permises par les technologies actuelles de communication et de stockage de l'information, les enjeux actuels résident dans le développement de méthodes d'analyse de données efficaces et adaptées aux systèmes étudiés. Cette thèse se propose de répondre à cette problématique, à la fois par des développements méthodologiques et par une application à des données réelles issues du système de vélos en libre-service Vélo'v à Lyon.Le système Vélo'v peut se représenter sous la forme d'un réseau, décrivant un ensemble de relations entre les différentes stations. Cette représentation, valable également pour de nombreux systèmes, permet l'utilisation d'outils pour décrire la structure du réseau basés sur la théorie des graphes. Néanmoins, la prise en compte d'une dynamique temporelle dans l'évolution des systèmes nécessite d'étendre l'analyse à des réseaux temporels, dont la structure évolue au cours du temps. Le parallèle avec le domaine du traitement du signal, dont le but est l'analyse de signaux temporels, amène à considérer des connexions entre la description de l'évolution d'un réseau temporel et celle d'un signal. Ces travaux proposent de considérer une dualité entre les réseaux temporels et les signaux, de sorte que l'analyse dans le domaine des signaux, à l'aide des outils du traitement du signal, permet de caractériser le réseau temporel correspondant.Cette méthodologie, à la frontière entre le traitement du signal et l'analyse des réseaux, est tout d'abord justifiée par l'étude du système Vélo'v, en comparant différentes approches d'analyse de données et les apports de la représentation sous la forme de réseau temporel. Une méthode d'étiquetage des noeuds d'un graphe est ensuite discutée, permettant d'ouvrir la voie vers une dualité entre réseaux et signaux. Cette dualité est étendue aux réseaux temporels, pour lesquels une méthode d'extraction automatique des structures pertinentes au cours du temps est proposée, à travers la décomposition des signaux correspondants
Bike-sharing systems have become essential elements in urban transportation systems of many world's big cities. Thanks to the data generated by these systems, it is possible to obtain a precise characterization of urban cycling, both in terms of transportation and socio-economic aspects. Taking advantage of the recent abundance of data allowed by the current technology, the challenges lie in the development of efficient data analysis method, adapted to these systems. This PhD thesis proposes some answers to this issue, first by methodological developments and second by studying real-world data obtained from the bike-sharing system in Lyon, called Vélo'v.The Vélo'v system can be represented as a network, describing a set of relations between the stations spread over the city. This representation, used for many systems, enables the use of tools from network theory to measure the network structure and understand the underlying mechanisms. Nevertheless, taking into account the dynamic evolution of the structure requires an extension of the classical tools to the temporal case. Parallels between this problem and the field of signal processing can be done, and opens the way to the consideration of connections between the description of the dynamics of temporal networks and those of signals. This work introduces a duality between temporal networks and signals, such that the analysis of the signals using the classical tools of signal processing helps to the characterization of the structure of the corresponding network.This methodology, at the juncture between signal processing and network analysis, is first justified by the study of the Vélo'v network, by comparing different data analysis method and the representation of the system as a temporal network. Then, a method to relabel the vertices of the graph according to the topology of the network is discussed, opening up a duality between networks and signals. This duality is then extended to temporal networks: The analysis of the spectral properties of the signals are studied through a fully automated extraction method, enabling the decomposition of relevant network structure over time
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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.

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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.
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Baranawal, Animesh. "Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5721.

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Graphs with temporal characteristics are increasingly becoming prominent. Their vertices, edges and attributes are annotated with a lifespan, allowing one to add or remove vertices and edges. Such graphs can grow to millions of vertices, billions of edges, and have months or years of data. Time-dependent algorithms such as temporal reachability and shortest paths are designed over such materialised graphs. These algorithms find important use-cases in digital contact tracing, optimising transit routes, and analysing information diffusion over temporal graphs. Interval-centric Computing Model (ICM) is a recent abstraction over temporal graphs, enabling intuitive development of temporal graph algorithms while ensuring efficient computation and communication. It uses a bulk-synchronous parallel model of execution with data-parallel computation on interval-vertices and message passing at superstep boundaries. To ease the design of temporal algorithms, ICM introduces a novel TimeWarp phase for temporally aligning messages and grouping them against vertex states. However, this warp operator is super-linear in time complexity with the number of messages received at a vertex. It also has additional overheads in the form of message replications. Further, in pipelining the computation and communication phases, ICM may create stale or redundant messages. This thesis primarily attempts to design techniques to mitigate these performance limitations of ICM, and also extends ICM toward incremental graph processing. We propose three different techniques to accelerate the execution model of ICM: Local Warp Unrolling (LU), Deferred Message Scatter (DS) and Windowed ICM (WICM). LU unrolls the messages processed in the TimeWarp phase to reduce the time complexity of the warp operator. DS results in lazy scatter operations that reduce redundant calls to messaging. WICM partitions the temporal graph along the temporal dimension and processes the sub-interval graphs in parts, ensuring proper carryover of vertex states. While LU and DS apply locally to each vertex, WICM is applicable at the global interval graph level and can be coupled with the other two techniques. While developing these techniques, we identify necessary constraints identifying the algorithms that can be modelled using the optimisations. Further, we also prove the equivalence of the new execution model to ICM's execution for a large class of temporal traversal algorithms. For WICM, not all temporal partitioning strategies give the same execution performance. Hence, we also develop heuristics that use statistics on the global graph topology with an analytical modelling of TimeWarp to determine the interval partitioning used with WICM. We extensively evaluate these optimisations for six large temporal graphs with up to 133M vertices, 5.5B edges and 365 snapshots, and six graph algorithms on an 10-node commodity cluster. LU+DS reduce the runtime of ICM by an average of 56%; WICM reduces the runtime by 48% on average over native ICM, and combining these techniques offers an average reduction of 61%. We also conduct experiments to confirm the effectiveness of the heuristic partitioning technique. We also present preliminary results on extending the WICM model to operate over a graph that arrives incrementally, by batching the incoming updates and forming a window out of them to be executed using the WICM. This also has the benefit of reducing the memory footprint since the entire historic graph does not need to be retained in memory.
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Book chapters on the topic "Temporal Graph Processing"

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Baumstark, Alexander, Muhammad Attahir Jibril, and Kai-Uwe Sattler. "Temporal Graph Processing in Modern Memory Hierarchies." In Advances in Databases and Information Systems, 103–16. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42914-9_8.

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Battistone, Francesco, Alfredo Petrosino, and Gabriella Sanniti di Baja. "GRUNTS: Graph Representation for UNsupervised Temporal Segmentation." In Image Analysis and Processing — ICIAP 2015, 225–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_21.

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Brisaboa, Nieves R., Diego Caro, Antonio Fariña, and M. Andrea Rodríguez. "A Compressed Suffix-Array Strategy for Temporal-Graph Indexing." In String Processing and Information Retrieval, 77–88. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11918-2_8.

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Dai, Qianwen, Fang Kong, and Qianying Dai. "Event Temporal Relation Classification Based on Graph Convolutional Networks." In Natural Language Processing and Chinese Computing, 393–403. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32236-6_35.

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Chen, Ya, Wanrong Jiang, Hao Fu, and Guiquan Liu. "Spatio-Temporal Dynamic Multi-graph Attention Network for Ride-Hailing Demand Prediction." In Neural Information Processing, 133–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92270-2_12.

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Wang, Huimu, Zhen Liu, Zhiqiang Pu, and Jianqiang Yi. "STGA-LSTM: A Spatial-Temporal Graph Attentional LSTM Scheme for Multi-agent Cooperation." In Neural Information Processing, 663–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63833-7_56.

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Ma, Jingyuan, Zhan Shi, Shang Liu, Wang Zhang, Yutong Wu, Fang Wang, and Dan Feng. "LSM-Subgraph: Log-Structured Merge-Subgraph for Temporal Graph Processing." In Web and Big Data, 477–94. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25158-0_39.

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Kang, Junjun, and Fang Kong. "DialogueTRGAT: Temporal and Relational Graph Attention Network for Emotion Recognition in Conversations." In Natural Language Processing and Chinese Computing, 460–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17120-8_36.

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Ouyang, Wenli, Yahong Zhang, Mingda Zhu, Xiuling Zhang, Hongye Chen, Yinghao Ren, and Wei Fan. "Interpretable Spatial-Temporal Attention Graph Convolution Network for Service Part Hierarchical Demand Forecast." In Natural Language Processing and Chinese Computing, 575–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32236-6_52.

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Wu, Huanhuan, Yunjian Zhao, James Cheng, and Da Yan. "Efficient Processing of Growing Temporal Graphs." In Database Systems for Advanced Applications, 387–403. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55699-4_24.

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Conference papers on the topic "Temporal Graph Processing"

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Araghi, Hesam, Massoud Babaie-Zadeh, and Sophie Achard. "Dynamic K-Graphs: an Algorithm for Dynamic Graph Learning and Temporal Graph Signal Clustering." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287661.

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Guillou, Liane, Sander Bijl de Vroe, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman. "Incorporating Temporal Information in Entailment Graph Mining." In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.textgraphs-1.7.

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Mostafa, Abdelrahman, Wei Peng, and Guoying Zhao. "Hyperbolic Spatial Temporal Graph Convolutional Networks." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897522.

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Wu, Jiapeng, Meng Cao, Jackie Chi Kit Cheung, and William L. Hamilton. "TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.462.

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Sun, Haohai, Shangyi Geng, Jialun Zhong, Han Hu, and Kun He. "Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs." In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.emnlp-main.507.

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Isufi, Elvin, Andreas Loukas, Andrea Simonetto, and Geert Leus. "Separable autoregressive moving average graph-temporal filters." In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760238.

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Dal Col, Alcebiades, and Luis Gustavo Nonato. "Visual Analytics via Graph Signal Processing." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8295.

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This dissertation presents an overview of the extension of the classical signal processing theory to graph domains. Furthermore, we introduce in this dissertation a novel method for visual analysis of dynamic networks, which relies on the graph wavelet theory. Our method enables the automatic analysis of a signal defined on the nodes of a network. We use a fast approximation of the graph wavelet transform to derive a set of wavelet coefficients, which are then used to identify activity patterns on large networks, including their temporal recurrence. The wavelet coefficients naturally encode spatial and temporal variations of the signal, leading to an efficient and meaningful representation. This method allows for the exploration of the structural evolution of the network and their patterns over time. The effectiveness of our approach is demonstrated using different scenarios and comparisons involving real dynamic networks.
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Guo, Jin, Zhen Han, Su Zhou, Jiliang Li, Volker Tresp, and Yuyi Wang. "Continuous Temporal Graph Networks for Event-Based Graph Data." In Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.dlg4nlp-1.3.

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Cheng, Zida, Siheng Chen, and Ya Zhang. "Spatio-Temporal Graph Complementary Scattering Networks." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747790.

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Yao, Jiarui, Steven Bethard, Kristin Wright-Bettner, Eli Goldner, David Harris, and Guergana Savova. "Textual Entailment for Temporal Dependency Graph Parsing." In Proceedings of the 5th Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.25.

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