Results 21 to 30 of about 323,205 (262)
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 For a long time, the vision community tries to learn the spatio-temporal representation by combining convolutional neural network together with various temporal models, such as the families of Markov chain, optical flow, RNN and temporal convolution. However, these pipelines consume enormous computing resources due to the alternately learning process ...
Hao Shao, Shengju Qian, Yu Liu
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Gaussian Embedding of Temporal Networks
IEEE Access, 2023 Representing the nodes of continuous-time temporal graphs in a low-dimensional latent space has wide-ranging applications, from prediction to visualization. Yet, analyzing continuous-time relational data with timestamped interactions introduces unique challenges due to its sparsity.
Raphaël Romero +4 more
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Temporal network embedding framework with causal anonymous walks representations [PDF]
PeerJ Computer Science, 2022 Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding.
Ilya Makarov +7 more
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Communicability in temporal networks [PDF]
Physical Review E, 2013 A first-principles approach to quantify the communicability between pairs of nodes in temporal networks is proposed. It corresponds to the imaginary-time propagator of a quantum random walk in the temporal network, which accounts for unique structural and temporal characteristics of both streaming and nonstreaming temporal networks.
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Temporal Network Creation Games
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023 Most networks are not static objects, but instead they change over time. This observation has sparked rigorous research on temporal graphs within the last years. In temporal graphs, we have a fixed set of nodes and the connections between them are only available at certain time steps. This gives rise to a plethora of algorithmic problems on such graphs,
Bilo' D. +6 more
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Research on K-truss Community Search Algorithm for Temporal Networks
Jisuanji kexue yu tansuo, 2020 In applications such as communication network, collaboration network and social network analysis, time stamps are usually included on the edge. However, most previous studies focus on identifying communities in networks without time information.
XU Lantian, LI Ronghua, WANG Guoren, WANG Biao
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Lyapunov exponents for temporal networks
Physical Review E, 2023 By interpreting a temporal network as a trajectory of a latent graph dynamical system, we introduce the concept of dynamical instability of a temporal network, and construct a measure to estimate the network Maximum Lyapunov Exponent (nMLE) of a temporal network trajectory.
Annalisa Caligiuri +4 more
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Temporalizing static graph autoencoders to handle temporal networks [PDF]
Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2021 Graph autoencoders (GAE), also known as graph embedding methods, learn latent representations of the nodes of a graph in a low-dimensional space where the structural information is preserved. While real-world graphs are generally dynamic, only a few embedding methods handle the temporal dimension: Even though they have proven their reliability, the ...
Haddad, Mounir +3 more
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Communicability in time-varying networks with memory
New Journal of Physics, 2022 We develop a first-principles approach to define the communicability between two nodes in a time-varying network with memory. The formulation is based on the time-fractional Schrödinger equation, where the fractional (of Caputo type) derivative accounts ...
Ernesto Estrada
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CoRR, 2019 Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real-world. However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks.
Nesreen K. Ahmed +2 more
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