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Latif, Atefeh, Alireza Hedayati, and Vahe Aghazarian. "Improving Link Prediction in Dynamic Co-authorship Social Networks." International Academic Journal of Science and Engineering 05, no. 01 (June 1, 2018): 222–40. http://dx.doi.org/10.9756/iajse/v5i1/1810020.
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Choudhury, Nazim. "Community-Aware Evolution Similarity for Link Prediction in Dynamic Social Networks." Mathematics 12, no. 2 (January 15, 2024): 285. http://dx.doi.org/10.3390/math12020285.
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The link prediction problem is a time-evolving model in network science that has simultaneously abetted myriad applications and experienced extensive methodological improvement. Inferring the possibility of emerging links in dynamic social networks, also known as the dynamic link prediction task, is complex and challenging. In contrast to the link prediction in cross-sectional networks, dynamic link prediction methods need to cater to the actor-level temporal changes and associated evolutionary information regarding their micro- (i.e., link formation/deletion) and mesoscale (i.e., community formation) network structure. With the advent of abundant community detection algorithms, the research community has examined community-aware link prediction strategies in static networks. However, the same task in dynamic networks where, apart from the actors and links among them, their community pattern is also dynamic, is yet to be explored. Evolutionary community-aware information, including the associated link structure and temporal neighborhood changes, can effectively be mined to build dynamic similarity metrics for dynamic link prediction. This study aims to develop and integrate such dynamic features with machine learning algorithms for link prediction tasks in dynamic social networks. It also compares the performances of these features against well-known similarity metrics (i.e., ResourceAllocation) for static networks and a time series-based link prediction strategy in dynamic networks. These proposed features achieved high-performance scores, representing them as prospective candidates for both dynamic link prediction tasks and modeling the network growth.
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Sun, Mengdi, and Minghu Tang. "A Review of Link Prediction Algorithms in Dynamic Networks." Mathematics 13, no. 5 (February 28, 2025): 807. https://doi.org/10.3390/math13050807.
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Dynamic network link prediction refers to the prediction of possible future links or the identification of missing links on the basis of historical information of dynamic networks. Link prediction aids people in exploring and analyzing complex change patterns in the real world and it could be applied in personalized recommendation systems, intelligence analysis, anomaly detection, and other fields. This paper aims to provide a comprehensive review of dynamic network link prediction. Firstly, dynamic networks are categorized into dynamic univariate networks and dynamic multivariate networks according to the changes in their sets. Furthermore, dynamic network link prediction algorithms are classified into regular sampling and irregular sampling by the method of network sampling. After summarizing and comparing the common datasets and evaluation indicators for dynamic network link prediction, we briefly review classic related algorithms in recent years, and classify them according to the network changes, sampling methods, underlying principles of algorithms, and other classification methods. Meanwhile, the basic ideas, advantages, and disadvantages of these algorithms are discussed in detail. The application fields and challenges in this area are also summarized. In the final summary of the paper, the future research directions such as link prediction in dynamic heterogeneous weighted networks and the security issues brought about by link prediction are discussed.
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Sheng-Guo Wang, Sheng-Guo Wang, Yong-Gang Liu Sheng-Guo Wang, and Tian-Wei Bai Yong-Gang Liu. "Dynamic Node Link Model of Hierarchical Edge Computing." 電腦學刊 32, no. 5 (October 2021): 222–32. http://dx.doi.org/10.53106/199115992021103205019.
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With the rise of the Internet of Things, edge computing has become one of the key technologies in Internet of Things solutions. In the context of the Industrial Internet of Things, hierarchical edge computing shows its advantages. This article focuses on hierarchical edge computing in the industrial Internet of Things scene, and studies the dynamic resource allocation of hierarchical edge computing networks. When using a hierarchical edge computing network with existing equipment, it is difficult to make changes to existing equipment. Therefore, this article uses queuing theory modeling analysis and proposes Dynamic Link Model based on Nodes Relation. Aiming at the hierarchical edge computing network, this model uses a method based on node connection relationship transfer to achieve load balancing of task flow and completes the dynamic allocation of computing resources in the network, and proposes a time experienced priority queue offloading strategy. The paper uses Java to achieve a dynamic link model experiment based on the connection relationship between nodes. The results show that this scheme has significant advantages in the global average delay of the system, and ensure the loss probability is reasonable within a certain limit.
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Choudhury, Nazim, and Shahadat Uddin. "Evolutionary Features for Dynamic Link Prediction in Social Networks." Applied Sciences 13, no. 5 (February 24, 2023): 2913. http://dx.doi.org/10.3390/app13052913.
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One of the inherent characteristics of dynamic networks is the evolutionary nature of their constituents (i.e., actors and links). As a time-evolving model, the link prediction mechanism in dynamic networks can successfully capture the underlying growth mechanisms of social networks. Mining the temporal patterns of dynamic networks has led researchers to utilise dynamic information for dynamic link prediction. Despite several methodological improvements in dynamic link prediction, temporal variations of actor-level network structure and neighbourhood information have drawn little attention from the network science community. Evolutionary aspects of network positional changes and associated neighbourhoods, attributed to non-connected actor pairs, may suitably be used for predicting the possibility of their future associations. In this study, we attempted to build dynamic similarity metrics by considering temporal similarity and correlation between different actor-level evolutionary information of non-connected actor pairs. These metrics then worked as dynamic features in the supervised link prediction model, and performances were compared against static similarity metrics (e.g., AdamicAdar). Improved performance is achieved by the metrics considered in this study, representing them as prospective candidates for dynamic link prediction tasks and to help understand the underlying evolutionary mechanism.
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Safdari, Hadiseh, Martina Contisciani, and Caterina De Bacco. "Reciprocity, community detection, and link prediction in dynamic networks." Journal of Physics: Complexity 3, no. 1 (February 28, 2022): 015010. http://dx.doi.org/10.1088/2632-072x/ac52e6.
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Abstract Many complex systems change their structure over time, in these cases dynamic networks can provide a richer representation of such phenomena. As a consequence, many inference methods have been generalized to the dynamic case with the aim to model dynamic interactions. Particular interest has been devoted to extend the stochastic block model and its variant, to capture community structure as the network changes in time. While these models assume that edge formation depends only on the community memberships, recent work for static networks show the importance to include additional parameters capturing structural properties, as reciprocity for instance. Remarkably, these models are capable of generating more realistic network representations than those that only consider community membership. To this aim, we present a probabilistic generative model with hidden variables that integrates reciprocity and communities as structural information of networks that evolve in time. The model assumes a fundamental order in observing reciprocal data, that is an edge is observed, conditional on its reciprocated edge in the past. We deploy a Markovian approach to construct the network’s transition matrix between time steps and parameters’ inference is performed with an expectation-maximization algorithm that leads to high computational efficiency because it exploits the sparsity of the dataset. We test the performance of the model on synthetic dynamical networks, as well as on real networks of citations and email datasets. We show that our model captures the reciprocity of real networks better than standard models with only community structure, while performing well at link prediction tasks.
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Li, Huikang, Yi Gao, Wei Dong, and Chun Chen. "Preferential Link Tomography in Dynamic Networks." IEEE/ACM Transactions on Networking 27, no. 5 (October 2019): 1801–14. http://dx.doi.org/10.1109/tnet.2019.2931047.
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Song, Y. M., C. Zhang, and Y. Q. Yu. "Neural Networks Based Active Vibration Control of Flexible Linkage Mechanisms." Journal of Mechanical Design 123, no. 2 (May 1, 2000): 266–71. http://dx.doi.org/10.1115/1.1348269.
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An investigation is presented into the neural networks based active vibration control of flexible linkage mechanisms. A smart mechanism featuring piezoceramic actuators and strain gauge sensors is designed. A nonlinear adaptive control strategy named Neural Networks based Direct Self-Tuning Control (NNBDSC) is employed to suppress the elastodynamic responses of the smart mechanism. To improve the initial robustness of the NNBDSC, the Dynamic Recurrent Neural Network (DRNN) controllers are designed off-line to approximate the inverse dynamics of the smart mechanism. Through on-line control, the strain crest of the flexible link is reduced 60 percent or so and the dynamic performance of the smart mechanism is improved significantly.
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Kiss, Istvan Z., Luc Berthouze, Timothy J. Taylor, and Péter L. Simon. "Modelling approaches for simple dynamic networks and applications to disease transmission models." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2141 (January 18, 2012): 1332–55. http://dx.doi.org/10.1098/rspa.2011.0349.
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In this paper a random link activation–deletion (RLAD) model is proposed that gives rise to a stochastically evolving network. This dynamic network is then coupled to a simple susceptible-infectious-suceptible ( SIS ) dynamics on the network, and the resulting spectrum of model behaviour is explored via simulation and a novel pairwise model for dynamic networks. First, the dynamic network model is systematically analysed by considering link-type independent and dependent network dynamics coupled with globally constrained link creation. This is done rigorously with some analytical results and we highlight where such analysis can be performed and how these simpler models provide a benchmark to test and validate full simulations. The pairwise model is used to study the interplay between SIS -type dynamics on the network and link-type-dependent activation–deletion. Assumptions of the pairwise model are identified and their implications interpreted in a way that complements our current understanding. Furthermore, we also discuss how the strong assumptions of the closure relations can lead to disagreement between the simulation and pairwise model. Unlike on a static network, the resulting spectrum of behaviour is more complex with the prevalence of infections exhibiting not only a single steady state, but also bistability and oscillations.
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Chen, Lei, Jing Zhang, and Li-Jun Cai. "Overlapping community detection based on link graph using distance dynamics." International Journal of Modern Physics B 32, no. 03 (January 22, 2018): 1850015. http://dx.doi.org/10.1142/s0217979218500157.
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The distance dynamics model was recently proposed to detect the disjoint community of a complex network. To identify the overlapping structure of a network using the distance dynamics model, an overlapping community detection algorithm, called L-Attractor, is proposed in this paper. The process of L-Attractor mainly consists of three phases. In the first phase, L-Attractor transforms the original graph to a link graph (a new edge graph) to assure that one node has multiple distances. In the second phase, using the improved distance dynamics model, a dynamic interaction process is introduced to simulate the distance dynamics (shrink or stretch). Through the dynamic interaction process, all distances converge, and the disjoint community structure of the link graph naturally manifests itself. In the third phase, a recovery method is designed to convert the disjoint community structure of the link graph to the overlapping community structure of the original graph. Extensive experiments are conducted on the LFR benchmark networks as well as real-world networks. Based on the results, our algorithm demonstrates higher accuracy and quality than other state-of-the-art algorithms.
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Abbas, Khushnood, Alireza Abbasi, Shi Dong, Ling Niu, Liyong Chen, and Bolun Chen. "A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks." Entropy 25, no. 2 (January 31, 2023): 257. http://dx.doi.org/10.3390/e25020257.
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Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach. Learning useful representations of nodes in these networks provides greater predictive power with less computational complexity and facilitates the use of machine learning methods. Considering that existing models fail to consider the temporal dimensions of the networks, this research proposes a novel temporal network-embedding algorithm for graph representation learning. This algorithm generates low-dimensional features from large, high-dimensional networks to predict temporal patterns in dynamic networks. The proposed algorithm includes a new dynamic node-embedding algorithm that exploits the evolving nature of the networks by considering a simple three-layer graph neural network at each time step and extracting node orientation by using Given’s angle method. Our proposed temporal network-embedding algorithm, TempNodeEmb, is validated by comparing it to seven state-of-the-art benchmark network-embedding models. These models are applied to eight dynamic protein–protein interaction networks and three other real-world networks, including dynamic email networks, online college text message networks, and human real contact datasets. To improve our model, we have considered time encoding and proposed another extension to our model, TempNodeEmb++. The results show that our proposed models outperform the state-of-the-art models in most cases based on two evaluation metrics.
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Huang, Xiaoli, Jingyu Li, and Yumiao Yuan. "Link Prediction in Dynamic Social Networks Combining Entropy, Causality, and a Graph Convolutional Network Model." Entropy 26, no. 6 (May 30, 2024): 477. http://dx.doi.org/10.3390/e26060477.
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Link prediction is recognized as a crucial means to analyze dynamic social networks, revealing the principles of social relationship evolution. However, the complex topology and temporal evolution characteristics of dynamic social networks pose significant research challenges. This study introduces an innovative fusion framework that incorporates entropy, causality, and a GCN model, focusing specifically on link prediction in dynamic social networks. Firstly, the framework preprocesses the raw data, extracting and recording timestamp information between interactions. It then introduces the concept of “Temporal Information Entropy (TIE)”, integrating it into the Node2Vec algorithm’s random walk to generate initial feature vectors for nodes in the graph. A causality analysis model is subsequently applied for secondary processing of the generated feature vectors. Following this, an equal dataset is constructed by adjusting the ratio of positive and negative samples. Lastly, a dedicated GCN model is used for model training. Through extensive experimentation in multiple real social networks, the framework proposed in this study demonstrated a better performance than other methods in key evaluation indicators such as precision, recall, F1 score, and accuracy. This study provides a fresh perspective for understanding and predicting link dynamics in social networks and has significant practical value.
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Cheng, Lily, John Ellson, Admela Jukan, Patrice Lamy, and Eve Varma. "Network engineering-Control of dynamic link topology in user networks." Bell Labs Technical Journal 8, no. 1 (July 9, 2003): 207–18. http://dx.doi.org/10.1002/bltj.10054.
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Gao, Hongwei, Han Qiao, Artem Sedakov, and Lei Wang. "A Dynamic Formation Procedure of Information Flow Networks." Journal of Systems Science and Information 3, no. 2 (April 25, 2015): 97–110. http://dx.doi.org/10.1515/jssi-2015-0097.
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AbstractA characterization of the equilibrium of information flow networks and the dynamics of network formation are studied under the premise of local information flow. The main result of this paper is that it gives the dynamic formation procedure in the local information flow network. The research shows that core-periphery structure is the most representative equilibrium network in the case of the local information flow without information decay whatever the cost of information is homogeneous or heterogeneous. If the profits and link costs of local information flow networks with information decay are homogeneous empty network and complete network are typical equilibrium networks, which are related to the costs of linking.
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Xu, Hai Hang, and Li Jun Zhang. "Application of Link Prediction in Temporal Networks." Advanced Materials Research 756-759 (September 2013): 2231–36. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2231.
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Link prediction is an important research hotspot in complex networks.Correlational studies merely use static topology for prediction, without considering the influence of network dynamic evolutionary process on link prediction. We believe that the linksare derived from the evolutionary process of network, and dynamic network topology will contain more information, Moreover, many networks have time attribute naturally, which is apt to combine the similarity of time and structure for link prediction. The paper proposes the concept of active factor using time attribute, to extend the similaritybased link prediction framework.Thenmodeland analysis the data of citation network and cooperation network with temporal networks.Design the active factors for both network sand verify the performance of these new indexes. The results shows that the indexes with active factor perform better than structure similarity based indexes.
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Bae, Jun Hyung, and Sang-Mook Lee. "Investigation of SIS epidemics on dynamic network models with temporary link deactivation control schemes." Mathematical Biosciences and Engineering 19, no. 6 (2022): 6317–30. http://dx.doi.org/10.3934/mbe.2022295.
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<abstract> <p>Mathematical modeling of epidemic diseases is increasingly being used to respond to emerging diseases. Although conditions modeled by SIS dynamics will eventually reach either a disease-free steady-state or an endemic steady state without interventions, it is desirable to eradicate the disease as quickly as possible by introducing a control scheme. Here, we investigate the control methods of epidemic models on dynamic networks with temporary link deactivation. A quick link deactivation mechanism can simulate a community effort to reduce the risk of infection by temporarily avoiding infected neighbors. Once infected individuals recover, the links between the susceptible and recovered are activated. Our study suggests that a control scheme that has been shown ineffective in controlling dynamic network models may yield effective responses for networks with certain types of link dynamics, such as the temporary link deactivation mechanisms. We observe that a faster and more effective eradication could be achieved by updating control schemes frequently.</p> </abstract>
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Ren, Weiwu, Jialin Zhu, Hui Qi, Ligang Cong, and Xiaoqiang Di. "Dynamic optimization of intersatellite link assignment based on reinforcement learning." International Journal of Distributed Sensor Networks 18, no. 2 (February 2022): 155014772110702. http://dx.doi.org/10.1177/15501477211070202.
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Intersatellite links can reduce the dependence of satellite communication systems on ground networks, reduce the number of ground gateways, and reduce the complexity and investment of ground networks, which are important future trends in satellite development. Intersatellite links are dynamic over time, and different intersatellite topologies have a great impact on satellite network performance. To improve the overall performance of satellite networks, a satellite link assignment optimization algorithm based on reinforcement learning is proposed in this article. Different from the swarm intelligence method in principle, this algorithm models the combinatorial optimization problem of links as the optimal sequence decision problem of a series of link selection actions. Realistic constraints such as intersatellite visibility, network connectivity, and number of antenna beams are regarded as fully observable environmental factors. The agent selects the link according to the decision, and the selection action utility affects the next selection decision. After a finite number of iterations, the optimal link assignment scheme with minimum link delay is achieved. The simulation results show that in 8 or 12 satellite network systems, compared with the original topology, the topology calculated by this method has better network delay and smaller delay variance.
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Nance, Richard E., and Robert L. Moose. "Link capacity assignment in dynamic hierarchical networks." Computer Networks and ISDN Systems 15, no. 3 (August 1988): 189–202. http://dx.doi.org/10.1016/0169-7552(88)90068-2.
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Carchiolo, Vincenza, Christian Cavallo, Marco Grassia, Michele Malgeri, and Giuseppe Mangioni. "Link Prediction in Time Varying Social Networks." Information 13, no. 3 (March 1, 2022): 123. http://dx.doi.org/10.3390/info13030123.
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Predicting new links in complex networks can have a large societal impact. In fact, many complex systems can be modeled through networks, and the meaning of the links depend on the system itself. For instance, in social networks, where the nodes are users, links represent relationships (such as acquaintance, friendship, etc.), whereas in information spreading networks, nodes are users and content and links represent interactions, diffusion, etc. However, while many approaches involve machine learning-based algorithms, just the most recent ones account for the topology of the network, e.g., geometric deep learning techniques to learn on graphs, and most of them do not account for the temporal dynamics in the network but train on snapshots of the system at a given time. In this paper, we aim to explore Temporal Graph Networks (TGN), a Graph Representation Learning-based approach that natively supports dynamic graphs and assigns to each event (link) a timestamp. In particular, we investigate how the TGN behaves when trained under different temporal granularity or with various event aggregation techniques when learning the inductive and transductive link prediction problem on real social networks such as Twitter, Wikipedia, Yelp, and Reddit. We find that initial setup affects the temporal granularity of the data, but the impact depends on the specific social network. For instance, we note that the train batch size has a strong impact on Twitter, Wikipedia, and Yelp, while it does not matter on Reddit.
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Sanna Passino, Francesco, Anna S. Bertiger, Joshua C. Neil, and Nicholas A. Heard. "Link prediction in dynamic networks using random dot product graphs." Data Mining and Knowledge Discovery 35, no. 5 (August 5, 2021): 2168–99. http://dx.doi.org/10.1007/s10618-021-00784-2.
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AbstractThe problem of predicting links in large networks is an important task in a variety of practical applications, including social sciences, biology and computer security. In this paper, statistical techniques for link prediction based on the popular random dot product graph model are carefully presented, analysed and extended to dynamic settings. Motivated by a practical application in cyber-security, this paper demonstrates that random dot product graphs not only represent a powerful tool for inferring differences between multiple networks, but are also efficient for prediction purposes and for understanding the temporal evolution of the network. The probabilities of links are obtained by fusing information at two stages: spectral methods provide estimates of latent positions for each node, and time series models are used to capture temporal dynamics. In this way, traditional link prediction methods, usually based on decompositions of the entire network adjacency matrix, are extended using temporal information. The methods presented in this article are applied to a number of simulated and real-world graphs, showing promising results.
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Zufiria, Pedro, and Iker Barriales-Valbuena. "Analysis of Basic Features in Dynamic Network Models." Entropy 20, no. 9 (September 7, 2018): 681. http://dx.doi.org/10.3390/e20090681.
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Time evolving Random Network Models are presented as a mathematical framework for modelling and analyzing the evolution of complex networks. This framework allows the analysis over time of several network characterizing features such as link density, clustering coefficient, degree distribution, as well as entropy-based complexity measures, providing new insight on the evolution of random networks. First, some simple dynamic network models, based only on edge density, are analyzed to serve as a baseline reference for assessing more complex models. Then, a model that depends on network structure with the aim of reflecting some characteristics of real networks is also analyzed. Such model shows a more sophisticated behavior with two different regimes, one of them leading to the generation of high clustering coefficient/link density ratio values when compared with the baseline values, as it happens in many real networks. Simulation examples are discussed to illustrate the behavior of the proposed models.
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Ozcan, Alper, and Sule Gunduz Oguducu. "Multivariate Time Series Link Prediction for Evolving Heterogeneous Network." International Journal of Information Technology & Decision Making 18, no. 01 (January 2019): 241–86. http://dx.doi.org/10.1142/s0219622018500530.
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Link prediction is considered as one of the key tasks in various data mining applications for recommendation systems, bioinformatics, security and worldwide web. The majority of previous works in link prediction mainly focus on the homogeneous networks which only consider one type of node and link. However, real-world networks have heterogeneous interactions and complicated dynamic structure, which make link prediction a more challenging task. In this paper, we have studied the problem of link prediction in the dynamic, undirected, weighted/unweighted, heterogeneous social networks which are composed of multiple types of nodes and links that change over time. We propose a novel method, called Multivariate Time Series Link Prediction for evolving heterogeneous networks that incorporate (1) temporal evolution of the network; (2) correlations between link evolution and multi-typed relationships; (3) local and global similarity measures; and (4) node connectivity information. Our proposed method and the previously proposed time series methods are evaluated experimentally on a real-world bibliographic network (DBLP) and a social bookmarking network (Delicious). Experimental results show that the proposed method outperforms the previous methods in terms of AUC measures in different test cases.
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Wang, Chenhao, Ju Lynn Ong, Amiya Patanaik, Juan Zhou, and Michael W. L. Chee. "Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states." Proceedings of the National Academy of Sciences 113, no. 34 (August 10, 2016): 9653–58. http://dx.doi.org/10.1073/pnas.1523980113.
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Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced within-network functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants’ eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal.
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Gupta, Shubham, Gaurav Sharma, and Ambedkar Dukkipati. "A Generative Model for Dynamic Networks with Applications." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7842–49. http://dx.doi.org/10.1609/aaai.v33i01.33017842.
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Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.
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Ding, Fuzhuang, Xu Li, and Yanan Liang. "Research on characterizing the high dynamic performance of distributed FANETs." Journal of Physics: Conference Series 2615, no. 1 (October 1, 2023): 012013. http://dx.doi.org/10.1088/1742-6596/2615/1/012013.
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Abstract Distributed Flying Ad-Hoc Networks (FANETs) have been widely used in collaborative reconnaissance, situation construction and other scenarios. Due to malicious damage, complex electromagnetic interference, and rapid movement of UAV nodes during flight, distributed FANET have high dynamic characteristics. However, most researches on the characterization of network dynamics cannot fully consider the influence of various factors, such as protocol self-interference. In this paper, by comprehensively considering the effects of node damage, channel fading, protocol self-interference and other factors, based on HCPP model, Weibull distribution model and other theories, we establish link outage probability of network model to characterize link randomness and high dynamicity. On this basis, link entropy rate and topology entropy rate model are established to characterize the high dynamic performance of distributed FANETs.
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Chen, Ke-Jia, Yang Chen, Yun Li, and Jingyu Han. "A supervised link prediction method for dynamic networks." Journal of Intelligent & Fuzzy Systems 31, no. 1 (June 13, 2016): 291–99. http://dx.doi.org/10.3233/ifs-162141.
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Kim, Yoonsoo. "Efficient identification of link importance in dynamic networks." Journal of the Franklin Institute 352, no. 9 (September 2015): 3716–29. http://dx.doi.org/10.1016/j.jfranklin.2014.11.011.
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Sarkar, Purnamrita, Deepayan Chakrabarti, and Michael Jordan. "Nonparametric link prediction in large scale dynamic networks." Electronic Journal of Statistics 8, no. 2 (2014): 2022–65. http://dx.doi.org/10.1214/14-ejs943.
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Cholvi, V. "Stability bounds in networks with dynamic link capacities." Information Processing Letters 109, no. 2 (December 2008): 151–54. http://dx.doi.org/10.1016/j.ipl.2008.09.020.
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Teng, Fei, Yuling Sun, Lu Wang, and Jiangfeng Liu. "Semantic-Based Link Prediction in Dynamic Technology Innovation Networks." Journal of Physics: Conference Series 2968, no. 1 (February 1, 2025): 012016. https://doi.org/10.1088/1742-6596/2968/1/012016.
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Abstract Technological innovation networks are essential for advancing technologies and fostering scientific breakthroughs. However, a significant challenge remains in their limited capacity to accurately capture semantic relationships and predict future technological developments. To address this, a dynamic innovation network for the technologies is constructed by integrating the LDA topic model and the word2vec algorithm, allowing for the exploration of its evolving patterns. Then, by optimizing the link prediction algorithm, potential relationships within the network are identified, revealing under-explored technological development directions. The results, focusing on hydrogen energy technology, indicate that technologies focused on enhancing the efficiency and performance of power systems, improving the reliability of hydrogen energy systems, and advancing high-efficiency electrolysis-based hydrogen production and supply systems exhibit sustained research momentum. Notably, high-efficiency electrolysis-based hydrogen production systems have experienced significant growth in research attention in recent years. It is anticipated that future advancements will involve greater integration and relevance of technologies aimed at improving power system efficiency, potentially catalyzing a leap development of hydrogen energy technologies.
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Zhang, Haici. "A Deep Learning Approach to Dynamic Interbank Network Link Prediction." International Journal of Financial Studies 10, no. 3 (July 12, 2022): 54. http://dx.doi.org/10.3390/ijfs10030054.
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Lehman Brothers’ failure in 2008 demonstrated the importance of understanding interconnectedness in interbank networks. The interbank market plays a significant role in facilitating market liquidity and providing short-term funding for each other to smooth liquidity shortages. Knowing the trading relationship could also help understand risk contagion among banks. Therefore, future lending relationship prediction is important to understand the dynamic evolution of interbank networks. To achieve the goal, we apply a deep learning framework model of interbank lending to an electronic trading interbank network for temporal trading relationship prediction. There are two important components of the model, which are the Graph convolutional network (GCN) and the Long short-term memory (LSTM) model. The GCN and LSTM components together capture the spatial–temporal information of the dynamic network snapshots. Compared with the Discrete autoregressive model and Dynamic latent space model, our proposed model achieves better performance in both the precrisis and the crisis period.
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Raaid, Raaid, Nisreen Abbas Hussein, and Raaid Alubady. "Machine Learning for Link Prediction between Nodes in Complex Networks." Fusion: Practice and Applications 18, no. 1 (2025): 41–55. https://doi.org/10.54216/fpa.180104.
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Recently, the complex network has become popular use as it can transfer huge amounts of multimedia, text, ideas, and other information, encouraging many participant connections. Social media is one of these networks that make the most connections. Predicting the formation or dissolution of links between nodes presents a problem for social network analysis researchers. Since social networks are dynamic, this task is exciting as it may also forecast lost network links with less information. On the other way, current link prediction methods use simply node similarity to find links. This study proposes a new technique that relies on node attributes and similarity measures. Nodes are labeled by their centrality and similarity. The network's edges are negative and positive samples. A well-defined dataset for link prediction comprises the features of the nodes at the edges labeled either positive or negative. The dataset is passed to multiple machine learning classifiers. On several real-world networks. The experiments conducted during the research show that Gradient Boosting gave the highest accuracy of 99% compared with other methods.
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Kurata, H. "CADLIVE dynamic simulator: Direct link of biochemical networks to dynamic models." Genome Research 15, no. 4 (March 21, 2005): 590–600. http://dx.doi.org/10.1101/gr.3463705.
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Long, Hao, Feng Hu, and Lingjun Kong. "Enhanced Link Prediction and Traffic Load Balancing in Unmanned Aerial Vehicle-Based Cloud-Edge-Local Networks." Drones 8, no. 10 (September 27, 2024): 528. http://dx.doi.org/10.3390/drones8100528.
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With the advancement of cloud-edge-local computing, Unmanned Aerial Vehicles (UAVs), as flexible mobile nodes, offer novel solutions for dynamic network deployment. However, existing research on UAV networks faces substantial challenges in accurately predicting link dynamics and efficiently managing traffic loads, particularly in highly distributed and rapidly changing environments. These limitations result in inefficient resource allocation and suboptimal network performance. To address these challenges, this paper proposes a UAV-based cloud-edge-local network resource elastic scheduling architecture, which integrates the Graph-Autoencoder–GAN-LSTM (GA–GLU) algorithm for accurate link prediction and the FlowBender-Enhanced Reinforcement Learning for Load Balancing (FERL-LB) algorithm for dynamic traffic load balancing. GA–GLU accurately predicts dynamic changes in UAV network topologies, enabling adaptive and efficient scheduling of network resources. FERL-LB leverages these predictions to optimize traffic load balancing within the architecture, enhancing both performance and resource utilization. To validate the effectiveness of GA–GLU, comparisons are made with classical methods such as CN and Katz, as well as modern approaches like Node2vec and GAE–LSTM, which are commonly used for link prediction. Experimental results demonstrate that GA–GLU consistently outperforms these competitors in metrics such as AUC, MAP, and error rate. The integration of GA–GLU and FERL-LB within the proposed architecture significantly improves network performance in highly dynamic environments.
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Malviya, Abhishek, and Sudhakar Pandey. "Clustering Based Dynamic Bandwidth Allocation in HC-RAN." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (November 30, 2022): 121–30. http://dx.doi.org/10.17762/ijritcc.v10i11.5788.
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A wireless network is composed of several independent nodes or gadgets that communicate mutually through a wireless link. The most destructive challenge encountered in a wireless network is bandwidth allocation because it defines the amount the network will cost and how effectively it will function. The most cutting-edge network architecture in the present wireless communication system, cluster-based heterogeneous cloud radio access networks (HC-RANs), is what powers cloud computing in heterogeneous networks. In this research, we proposed an HC-RANs that may optimize energy consumption for wireless data transfer in the multi-hop device to device scenario. The proposed scheme offers bandwidth allocation in wireless environments where there are concerns about significant user mobility over the course of a given time. The above design, we used clustering with joint beam formation for the down link of heterogeneous cloud radio access network (HC-RAN), developed design to improved amount of FBS. Result outcomes helped in calculating Critical bandwidth usage (CBU).
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Meng, Fanrong, Feng Zhang, Mu Zhu, Yan Xing, Zhixiao Wang, and Jihong Shi. "Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/1873504.
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Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.
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Jin, Rencheng, Xinyuan Zhang, Jiajun Liu, Guangxu Wang, and Di Zhang. "A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs." Sensors 24, no. 5 (March 6, 2024): 1702. http://dx.doi.org/10.3390/s24051702.
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The Multi-Point Relay (MPR) is one of the core technologies for Optimizing Link State Routing (OLSR) protocols, offering significant advantages in reducing network overhead, enhancing throughput, maintaining network scalability, and adaptability. However, due to the restriction that only MPR nodes can forward control messages in the network, the current evaluation criteria for selecting MPR nodes are relatively limited, making it challenging to flexibly choose MPR nodes based on current link states in dynamic networks. Therefore, the selection of MPR nodes is crucial in dynamic networks. To address issues such as unstable links, poor transmission accuracy, and lack of real-time performance caused by mobility in dynamic networks, we propose a comprehensive evaluation algorithm of MPR based on link-state awareness. This algorithm defines five state evaluation parameters from the perspectives of node mobility and load. Subsequently, we use the entropy weight method to determine weight coefficients and employing the method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for comprehensive evaluation to select MPR nodes. Finally, the Comprehensive Evaluation based on Link-state awareness of OLSR (CEL-OLSR) protocol is proposed, and simulated experiments are conducted using NS-3. The results indicate that, compared to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, CEL-OLSR significantly improves network performance in terms of packet delivery rate, average end-to-end delay, network throughput, and control overhead.
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Ghasemi, Abdorasoul, and Holger Kantz. "Higher-order interaction learning of line failure cascading in power networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 7 (July 2022): 073101. http://dx.doi.org/10.1063/5.0089780.
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Line failure cascading in power networks is a complex process that involves direct and indirect interactions between lines’ states. We consider the inverse problem of learning statistical models to find the sparse interaction graph from the pairwise statistics collected from line failures data in the steady states and over time. We show that the weighted [Formula: see text]-regularized pairwise maximum entropy models successfully capture pairwise and indirect higher-order interactions undistinguished by observing the pairwise statistics. The learned models reveal asymmetric, strongly positive, and negative interactions between the network’s different lines’ states. We evaluate the predictive performance of models over independent trajectories of failure unfolding in the network. The static model captures the failures’ interactions by maximizing the log-likelihood of observing each link state conditioned to other links’ states near the steady states. We use the learned interactions to reconstruct the network’s steady states using the Glauber dynamics, predicting the cascade size distribution, inferring the co-susceptible line groups, and comparing the results against the data. The dynamic interaction model is learned by maximizing the log-likelihood of the network’s state in state trajectories and can successfully predict the network state for failure propagation trajectories after an initial failure.
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Savva, Andreas G., Theocharis Theocharides, and Vassos Soteriou. "Intelligent On/Off Dynamic Link Management for On-Chip Networks." Journal of Electrical and Computer Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/107821.
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Networks-on-chips (NoCs) provide scalable on-chip communication and are expected to be the dominant interconnection architectures in multicore and manycore systems. Power consumption, however, is a major limitation in NoCs today, and researchers have been constantly working on reducing both dynamic and static power. Among the NoC components, links that connect the NoC routers are the most power-hungry components. Several attempts have been made to reduce the link power consumption at both the circuit level and the system level. Most past research efforts have proposed selective on/off link state switching based on system-level information based on link utilization levels. Most of these proposed algorithms focus on a pessimistic and simple static threshold mechanism which determines whether or not a link should be turned on/off. This paper presents an intelligent dynamic power management policy for NoCs with improved predictive abilities based on supervised online learning of the system status (i.e., expected future utilization link levels), where links are turned off and on via the use of a small and scalable neural network. Simulation results with various synthetic traffic models over various network topologies show that the proposed work can reach up to 13% power savings when compared to a trivial threshold computation, at very low (<4%) hardware overheads.
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PANDURANGAN, GOPAL, and ELI UPFAL. "STATIC AND DYNAMIC EVALUATION OF QoS PROPERTIES." Journal of Interconnection Networks 01, no. 02 (June 2000): 135–50. http://dx.doi.org/10.1142/s0219265900000093.
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Efficient utilization of modern high bandwidth communication networks relies on statistical multiplexing of many logical channels through one physical channel. Communication requests typically include some statistical characterization of the requested connection (such as pick value, mean values, etc). The task of the network management procedure is to accommodate as many communication requests as possible while keeping the failure (e.g. overflow) probability bounded by a pre-specified parameter. When the network consists of one link, the task reduces to evaluating the probability that a sum of random variables does not exceed a given bound. Techniques such as the method of effective bandwidth give a practical solution for the one link problem. In this paper we address the more realistic setting of estimating QoS properties of multi-link networks with arbitrary patterns. The related optimization problem for that setting is #P-complete even for the most simple communication characteristics. Our main result is an efficient Monte-Carlo method for estimating the failure probability of a general network. Our method is particularly useful in a dynamic setting in which communication requests are dynamically added and eliminated from the system. The amortized cost in our solution of updating the estimate after each change is proportional to the fraction of links involved in the change rather than to the total number of links in the network.
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Supittayapornpong, Sucha, and Poompat Saengudomlert. "Joint Flow Control, Routing and Medium Access Control in Random Access Multi-Hop Wireless Networks with Time Varying Link Capacities." ECTI Transactions on Electrical Engineering, Electronics, and Communications 8, no. 1 (August 1, 2009): 22–31. http://dx.doi.org/10.37936/ecti-eec.201081.171988.
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This work extends the existing static framework for joint flow control, routing and medium access control (MAC) in random access multi-hop wireless networks to a dynamic framework where link capacities vary over time. The overall problem is formulated as a long term network utility maximization (NUM) problem (instead of the existing static NUM problem) that accounts for link capacity variation. This dynamic formulation is more realistic than the static one, and is one step closer to practical networks. Under the stationary and ergodic assumptions on the link capacity variation, the problem is decomposed to form a distributed algorithm. The algorithm samples current link capacities while it is iteratively and locally updating flow rates and link transmission probabilities. Simulation results demonstrate the ability of the algorithm to sustain the optimal average data rates despite the link capacity variation.
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Tesfaw, Belayneh Abebe, Rong-Terng Juang, Li-Chia Tai, Hsin-Piao Lin, Getaneh Berie Tarekegn, and Kabore Wendenda Nathanael. "Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System." Sensors 23, no. 19 (September 23, 2023): 8041. http://dx.doi.org/10.3390/s23198041.
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In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as helpful technologies to enhance UAV communication networks. However, due to the high mobility of UAVs, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. The simulation results showed that the proposed GRU model can effectively and accurately estimate the link quality of ground users in the RIS-assisted UAV-enabled wireless communication network.
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Muñoz, Pablo, Isabel de la Bandera, Fernando Ruiz, Salvador Luna-Ramírez, Raquel Barco, Matías Toril, Pedro Lázaro, and Jaime Rodríguez. "Computationally-Efficient Design of a Dynamic System-Level LTE Simulator." International Journal of Electronics and Telecommunications 57, no. 3 (September 1, 2011): 347–58. http://dx.doi.org/10.2478/v10177-011-0047-2.
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Computationally-Efficient Design of a Dynamic System-Level LTE SimulatorThe Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a new flat radio-network architecture and a significant increase in spectrum efficiency. In this paper, a computationally-efficient tool for dynamic system-level LTE simulations is proposed. A physical layer abstraction is performed to predict link-layer performance with a low computational cost. At link layer, there are two important functions designed to increase the network capacity: Link Adaptation and Dynamic Scheduling. Other Radio Resource Management functionalities such as Admission Control and Mobility Management are performed at network layer. The simulator is conceived for large simulated network time to allow evaluation of optimization algorithms for the main network-level functionalities.
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Shivshankar Rajput, Anil Pimpalapure, and Praveen Bhanodia. "Machine learning approach for link predic-tion in large stochastic online social net-works (SOSNs)." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 1, no. 1 (July 31, 2024): 12–20. https://doi.org/10.63503/j.ijaimd.2024.6.
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Social networks are growing every day at a tremendous pace. A social network is an online community that allows users to interact and exchange ideas, in-formation, activities, and interests. Millions of users are contributing to its character and behaviour, and the information being generated has a multi-tude of dimensional aspects that provide new opportunities and perspectives for the computation of network properties. Every individual quickly spreads messages and information throughout all linked groupings. Social networks give users the ability to make a profile, connect with others, and communicate with them through a variety of tools like chatting, updating their status, leav-ing comments, and sharing text, images, audio, and animated videos. With billions of users globally, social media platforms have emerged as an essential part of contemporary social interaction and communication. Along with changing how individuals share and consume information, they have also had a big impact on social, political, and cultural developments. These days, social communities, governmental agencies, and commercial enterprises all depend heavily on online social networks. Understanding networks' evolutionary na-ture requires the ability to predict missing links in existing networks and emerging or broken linkages in future networks. Since SNs undergo dynamic changes over time, link inference in these networks is an extremely difficult task. The dynamic character of supernovae is not well-accounted for in link prediction techniques. In this study, link prediction techniques in dynamic SNs will be thoroughly reviewed, analysed, discussed, and evaluated.
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S. Raj, Jennifer. "A Novel Hybrid Secure Routing for Flying Ad-hoc Networks." Journal of Trends in Computer Science and Smart Technology 2, no. 3 (August 5, 2020): 155–64. http://dx.doi.org/10.36548/jtcsst.2020.3.005.
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The recent technology development increases the opportunity to create valuable network services to user. Flying ad-hoc networks (FANET) in one among them which evolved recently with enhanced value added services with common features similar to its predecessor ad-hoc networks like vehicular ad hoc networks (VANET) and mobile ad hoc networks (MANET). Due to its distinctive features FANETs are widely preferred in recent telecommunication services which requires high quality of services, efficiency, environment adaptability and scalability. In order to achieve high efficiency multiple aerial vehicles are used in general architectures. The Communication in such vehicles are progressed directly between the nodes or through relay nodes. Routing is an important process to establish a connection link between the nodes in the architecture. This research work proposed a routing strategy suitable for dynamic and static environments as a hybrid optimization model which reduces the issues in link establishment. Nature inspired bee colony optimization is used along with conventional routing algorithms such as optimized link state routing protocol and Dynamic Source Routing Protocol to improve the link discovery. The proposed optimized routing outperforms well in reduced delay and communication overhead of the network.
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Pulyala, Ranjith. "Localized Network Reconfiguration Plan for Wireless Mesh Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 779–805. http://dx.doi.org/10.22214/ijraset.2022.41305.
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Abstract: Wireless mesh networks (WMNs) have emerged as a key technology for next-generation wireless networking. Because of their advantages over other wireless networks, WMNs are undergoing rapid progress and inspiring numerous applications. In multi-hop wireless mesh networks (WMNs) experience frequent link failures caused by channel interference, dynamic obstacles and/or applications’ bandwidth demands. These failures cause severe performance degradation in WMNs or require expensive, manual network management for their real-time recovery. This paper presents an Autonomous network Reconfiguration System (ARS) that enables a multi-radio WMN to autonomously recover from local link failures to preserve network performance. ARS also improves channel efficiency by more than 90% over the other recovery methods. During their lifetime, multi-hop wireless mesh networks (WMNs) experience frequent link failures caused by channel interference, dynamic obstacles, and/or applications’ bandwidth demands. These failures cause severe performance degradation in WMNs or require expensive manual network management for their real-time recovery. By using channel and radio diversities in WMNs, ARS generates necessary changes in local radio and channel assignments in order to recover from failures. Next, based on the thus-generated configuration changes, the system cooperatively reconfigures network settings among local mesh routers. ARS has been evaluated extensively through ns2-based simulation. Our evaluation results show that ARS outperforms existing failure-recovery schemes in improving channel-efficiency by more than 90% and in the ability of meeting the applications’ bandwidth demands by an average of 200%. Keywords: WMN, ARS, AODV, WCETT, ETT, RSVP, DSDV, ETX, ETOPE, BLC
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Balashov, Vasily V., Valery A. Kostenko, and Tatiana I. Ermakova. "Design of Onboard Real-Time Networks Based on SDN Technology." Modeling and Analysis of Information Systems 26, no. 1 (March 15, 2019): 23–38. http://dx.doi.org/10.18255/1818-1015-2019-1-23-38.
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Modern onboard equipment complexes (OEC) utilize AFDX and FC-AE-ASM-RT switched networks implementing a virtual link-based approach to real-time data transfer. The main drawback of these networks is their limited or absent support for dynamic reconfiguration of virtual links, which makes impossible the dynamical recomposition of OEC operation modes, particularly in case of multiple equipment failures. To remove these drawbacks, in this paper an approach is proposed to use software-defined networks (SDN) as onboard real-time networks. The proposed approach is based on implementation of a virtual link-based technology (similar to those used in AFDX and FC-AE-ASMRT) in an SDN supporting OpenFlow 1.3 protocol. The approach was implemented as a functional prototype and experimentally evaluated in a virtual network environment based on Ofsoftswitch13 software SDN switches and RUNOS controller. The experiments indicated that the proposed data exchange scheme allows the transfer of messages within the given limits on delay and jitter, and does not allow violation of constraints on a virtual link bandwidth. The experiments also confirmed that dynamic reconfiguration of virtual links in SDN does not interrupt the data transfer through unchanged virtual links. An important direction for future work is development of algorithms for dynamic creation of virtual link routes in course of OEC reconfiguration. The final goal of the work is to create an SDN-based network technology supporting both real-time data transfer and automatic network reconfiguration in case of OEC mode change, including parrying multiple failures.
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Çelikkanat, Abdulkadir, Nikolaos Nakis, and Morten Mørup. "Continuous-Time Graph Representation with Sequential Survival Process." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (March 24, 2024): 11177–85. http://dx.doi.org/10.1609/aaai.v38i10.28995.
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Over the past two decades, there has been a tremendous increase in the growth of representation learning methods for graphs, with numerous applications across various fields, including bioinformatics, chemistry, and the social sciences. However, current dynamic network approaches focus on discrete-time networks or treat links in continuous-time networks as instantaneous events. Therefore, these approaches have limitations in capturing the persistence or absence of links that continuously emerge and disappear over time for particular durations. To address this, we propose a novel stochastic process relying on survival functions to model the durations of links and their absences over time. This forms a generic new likelihood specification explicitly accounting for intermittent edge-persistent networks, namely GraSSP: Graph Representation with Sequential Survival Process. We apply the developed framework to a recent continuous time dynamic latent distance model characterizing network dynamics in terms of a sequence of piecewise linear movements of nodes in latent space. We quantitatively assess the developed framework in various downstream tasks, such as link prediction and network completion, demonstrating that the developed modeling framework accounting for link persistence and absence well tracks the intrinsic trajectories of nodes in a latent space and captures the underlying characteristics of evolving network structure.
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Shao, Min Hua, and Yi Ping Lee. "An Adaptive Link-Disjoint Multipath Routing in Ad Hoc Networks." Advanced Materials Research 171-172 (December 2010): 628–31. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.628.
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Mobile Ad Hoc Networks (MANET) are different from other network features, every mobile node possesses the role of host and router which makes routing mechanism become a key of the influence network stability. Because MANET topology is dynamic and easy-changing, so many scholars provide multipath and backup path routing protocol to solve the problems above. In 2001, Marina and Das proposed AOMDV multipath routing protocol which solves the limit of AODV single path routing protocol and promoted the efficiency of the whole network operation. However, we find out AOMDV has more than one first hop problem that cannot operate the multiple paths in some sparse networks. Thus, this paper uses the idea of common link to solve the problem which is an adaptive and efficient multipath routing protocol. To correspond to different dynamic topology, we need to adjust to route establishing scheme, in order to efficiently increase the number of paths and to decrease the frequency of route discovery which will provide a stable network connection. Finally, through the NS-2 simulation of experimental results shows our scheme the better performance in end to end delay and package delivery rate, compared with AOMDV routing protocol, and our scheme does not cause the additional network overhead.
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Liu, Qiang, Caihong Liu, Jiajia Wang, Xiang Wang, Bin Zhou, and Peng Zou. "Evolutionary link community structure discovery in dynamic weighted networks." Physica A: Statistical Mechanics and its Applications 466 (January 2017): 370–88. http://dx.doi.org/10.1016/j.physa.2016.09.028.
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