Results 31 to 40 of about 4,648,552 (299)
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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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
doaj +1 more source
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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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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Transitive reduction of citation networks [PDF]
, 2014 In many complex networks, the vertices are ordered in time, and edges represent causal connections. We propose methods of analysing such directed acyclic graphs taking into account the constraints of causality and highlighting the causal structure.
Tamar V. Loach +7 more
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Embedding and Trajectories of Temporal Networks
IEEE Access, 2023 Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a
Chanon Thongprayoon +2 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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Temporal interactions facilitate endemicity in the susceptible-infected-susceptible epidemic model
New Journal of Physics, 2016 Data of physical contacts and face-to-face communications suggest temporally varying networks as the media on which infections take place among humans and animals.
Leo Speidel +3 more
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Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks during Optimization [PDF]
Applied Sciences, 2022 Temporal network analysis and time evolution of network characteristics are powerful tools in describing the changing topology of dynamic networks. This paper uses such approaches to better visualize and provide analytical measures for the changes in performance that we observed in Voronoi-type spatial coverage, particularly for the example of time ...
Nicholas S. DiBrita +4 more
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