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Relevant bibliographies by topics / Vertex Reinforced Random Walk

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Academic literature on the topic 'Vertex Reinforced Random Walk'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Vertex Reinforced Random Walk"

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Pemantle, Robin. "Vertex-reinforced random walk." Probability Theory and Related Fields 92, no. 1 (1992): 117–36. http://dx.doi.org/10.1007/bf01205239.

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Volkov, Stanislav. "Vertex-reinforced random walk on arbitrary graphs." Annals of Probability 29, no. 1 (2001): 66–91. http://dx.doi.org/10.1214/aop/1008956322.

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Pemantle, Robin, and Stanislav Volkov. "Vertex-Reinforced Random Walk on Z Has Finite Range." Annals of Probability 27, no. 3 (1999): 1368–88. http://dx.doi.org/10.1214/aop/1022677452.

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Benaïm, Michel, and Pierre Tarrès. "Dynamics of vertex-reinforced random walks." Annals of Probability 39, no. 6 (2011): 2178–223. http://dx.doi.org/10.1214/10-aop609.

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Sabot, Christophe, and Pierre Tarrès. "Edge-reinforced random walk, vertex-reinforced jump process and the supersymmetric hyperbolic sigma model." Journal of the European Mathematical Society 17, no. 9 (2015): 2353–78. http://dx.doi.org/10.4171/jems/559.

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Dai, Jack Jie. "Some results regarding vertex-reinforced random walks." Statistics & Probability Letters 66, no. 3 (2004): 259–66. http://dx.doi.org/10.1016/j.spl.2003.10.017.

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Dai, Jack Jie. "A note on vertex-reinforced random walks." Statistics & Probability Letters 62, no. 3 (2003): 275–80. http://dx.doi.org/10.1016/s0167-7152(03)00014-2.

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Tarrès, Pierre. "Vertex-reinforced random walk on ℤ eventually gets stuck on five points". Annals of Probability 32, № 3B (2004): 2650–701. http://dx.doi.org/10.1214/009117907000000694.

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Chen, Jun, and Gady Kozma. "Vertex-reinforced random walk on with sub-square-root weights is recurrent." Comptes Rendus Mathematique 352, no. 6 (2014): 521–24. http://dx.doi.org/10.1016/j.crma.2014.03.019.

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Basdevant, Anne-Laure, Bruno Schapira, and Arvind Singh. "Localization of a vertex reinforced random walk on $$\mathbb{Z }$$ with sub-linear weight." Probability Theory and Related Fields 159, no. 1-2 (2013): 75–115. http://dx.doi.org/10.1007/s00440-013-0502-3.

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Dissertations / Theses on the topic "Vertex Reinforced Random Walk"

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Renlund, Henrik. "Reinforced Random Walk." Thesis, Uppsala University, Department of Mathematics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121389.

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Li, Dan. "Shortest paths through a reinforced random walk." Thesis, Uppsala universitet, Analys och tillämpad matematik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-153802.

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Ma, Qi. "Reinforcement in Biology : Stochastic models of group formation and network construction." Doctoral thesis, Uppsala universitet, Analys och tillämpad matematik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-186989.

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Empirical studies show that similar patterns emerge from a large number of different biological systems. For example, the group size distributions of several fish species and house sparrows all follow power law distributions with an exponential truncation. Networks built by ant colonies, slime mold and those are designed by engineers resemble each other in terms of structure and transportation efficiency. Based on the investigation of experimental data, we propose a variety of simple stochastic models to unravel the underlying mechanisms which lead to the collective phenomena in different syst

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Le, Goff Line. "Formation spontanée de chemins : des fourmis aux marches aléatoires renforcées." Thesis, Paris 10, 2014. http://www.theses.fr/2014PA100180/document.

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Cette thèse est consacrée à la modélisation de la formation spontanée de chemins préférentiels par des marcheurs déposant des traces attractives sur leurs trajectoires. Plus précisément, par une démarche pluridisciplinaire couplant modélisation et expérimentation, elle vise à dégager un ensemble de règles minimales individuelles permettant l'apparition d'un tel phénomène. Dans ce but, nous avons étudié sous différents angles les modèles minimaux que sont les marches aléatoires renforcées (MAR).Ce travail comporte deux parties principales. La première démontre de nouveaux résultats dans le doma

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Artemenko, Igor. "On Weak Limits and Unimodular Measures." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/30417.

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In this thesis, the main objects of study are probability measures on the isomorphism classes of countable, connected rooted graphs. An important class of such measures is formed by unimodular measures, which satisfy a certain equation, sometimes referred to as the intrinsic mass transport principle. The so-called law of a finite graph is an example of a unimodular measure. We say that a measure is sustained by a countable graph if the set of rooted connected components of the graph has full measure. We demonstrate several new results involving sustained unimodular measures, and provide thorou

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Nandanwar, Sharad. "Recommendations in Complex Networks: Unifying Structure into Random Walk." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4950.

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Making recommendations or predicting links which are likely to exist in the future is one of the central problems in network science and graph mining. In spite of modern state-of- the-art approaches for link prediction, the traditional approaches like Resource Allocation Index, Adamic Adar still fi nd heavy use, because of their simplistic nature yet competitive performance. Our preliminary investigation reveals that a major fraction of missing links which are observed in the near future, are the links which are between one-hop distant nodes. Current \friend of friend is a friend" based

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Book chapters on the topic "Vertex Reinforced Random Walk"

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Xiao, Wenyi, Huan Zhao, Vincent W. Zheng, and Yangqiu Song. "Vertex-reinforced Random Walk for Network Embedding." In Proceedings of the 2020 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.67.

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Holmes, Mark, and Daniel Kious. "A Monotonicity Property for Once Reinforced Biased Random Walk on $$\mathbb {Z}^d$$." In Springer Proceedings in Mathematics & Statistics. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0302-3_10.

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Dai, Qionghai, and Yue Gao. "Typical Hypergraph Computation Tasks." In Artificial Intelligence: Foundations, Theory, and Algorithms. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_5.

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AbstractAfter hypergraph structure generation for the data, the next step is how to conduct data analysis on the hypergraph. In this chapter, we introduce four typical hypergraph computation tasks, including label propagation, data clustering, imbalance learning, and link prediction. The first typical task is label propagation, which is to predict the labels for the vertices, i.e., assigning a label to each unlabeled vertex in the hypergraph, based on the labeled information. In general cases, label propagation is to propagate the label information from labeled vertices to unlabeled vertices through structural information of the hyperedges. In this part, we discuss the hypergraph cut on hypergraphs and random walk interpretation of label propagation on hypergraphs. The second typical task is data clustering, which is formulated as dividing the vertices into several parts in a hypergraph. In this part, we introduce a hypergraph Laplacian smoothing filter and an embedded model for hypergraph clustering tasks. The third typical task is cost-sensitive learning, which targets on learning with different mis-classification costs. The fourth typical task is link prediction, which aims to discover missing relations or predict new coming hyperedges based on the observed hypergraph.

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Zhou, Yifei, and Conor Hayes. "Graph-Based Diffusion Method for Top-N Recommendation." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_23.

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AbstractData that may be used for personalised recommendation purposes can intuitively be modelled as a graph. Users can be linked to item data; item data may be linked to item data. With such a model, the task of recommending new items to users or making new connections between items can be undertaken by algorithms designed to establish the relatedness between vertices in a graph. One such class of algorithm is based on the random walk, whereby a sequence of connected vertices are visited based on an underlying probability distribution and a determination of vertex relatedness established. A diffusion kernel encodes such a process. This paper demonstrates several diffusion kernel approaches on a graph composed of user-item and item-item relationships. The approach presented in this paper, RecWalk*, consists of a user-item bipartite combined with an item-item graph on which several diffusion kernels are applied and evaluated in terms of top-n recommendation. We conduct experiments on several datasets of the RecWalk* model using combinations of different item-item graph models and personalised diffusion kernels. We compare accuracy with some non-item recommender methods. We show that diffusion kernel approaches match or outperform state-of-the-art recommender approaches.

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Dorogovtsev, Sergey N., and José F. F. Mendes. "Temporal Networks." In The Nature of Complex Networks. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780199695119.003.0011.

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Abstract When a process takes place on an evolving network or this network serves as an evolving substrate of a dynamical system, two time scales naturally emerge: (i) the shortest time of structural changes in a local neighbourhood of each vertex, and (ii) the shortest time (time step) of a process. The notion of a temporal network assumes that local structural changes in an evolving network occur faster than the time step of a process or that these two time scales are comparable. The simplest example of such structural changes is sufficiently frequent emergence and disappearance of edges in a network. A standard example of a process on a network is a random walk, whose shortest time scale is the minimal time a walker stays on a vertex between two moves. Loosely speaking, a temporal network changes locally faster than a process on it or with equal speed. Still, this state of a network can be steady.

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Conference papers on the topic "Vertex Reinforced Random Walk"

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Wu, Jun, Jingrui He, and Yongming Liu. "ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622603.

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Ma, Zongjie, Yi Fan, Kaile Su, Chengqian Li, and Abdul Sattar. "Random Walk in Large Real-World Graphs for Finding Smaller Vertex Cover." In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2016. http://dx.doi.org/10.1109/ictai.2016.0109.

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Chen, Yun-Nung, and Florian Metze. "Two-layer mutually reinforced random walk for improved multi-party meeting summarization." In 2012 IEEE Spoken Language Technology Workshop (SLT 2012). IEEE, 2012. http://dx.doi.org/10.1109/slt.2012.6424268.

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Andrade, Matheus Guedes de, Franklin De Lima Marquezino, and Daniel Ratton Figueiredo. "Characterizing the Relationship Between Unitary Quantum Walks and Non-Homogeneous Random Walks." In Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/ctd.2021.15756.

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Quantum walks on graphs are ubiquitous in quantum computing finding a myriad of applications. Likewise, random walks on graphs are a fundamental building block for a large number of algorithms with diverse applications. While the relationship between quantum and random walks has been recently discussed in specific scenarios, this work establishes a formal equivalence between the two processes on arbitrary finite graphs and general conditions for shift and coin operators. It requires empowering random walks with time heterogeneity, where the transition probability of the walker is non-uniform a

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Fan, Yi, Nan Li, Chengqian Li, Zongjie Ma, Longin Jan Latecki, and Kaile Su. "Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/87.

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The Maximum Vertex Weight Clique (MVWC) problem is NP-hard and also important in real-world applications. In this paper we propose to use the restart and the random walk strategies to improve local search for MVWC. If a solution is revisited in some particular situation, the search will restart. In addition, when the local search has no other options except dropping vertices, it will use random walk. Experimental results show that our solver outperforms state-of-the-art solvers in DIMACS and finds a new best-known solution. Also it is the unique solver which is comparable with state-of-the-art

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Chen, Yun-Nung, and Florian Metze. "Multi-layer mutually reinforced random walk with hidden parameters for improved multi-party meeting summarization." In Interspeech 2013. ISCA, 2013. http://dx.doi.org/10.21437/interspeech.2013-140.

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