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On the nature and use of models in network neuroscience
DS Bassett, P Zurn, JI Gold - Nature Reviews Neuroscience, 2018 - nature.com
Network theory provides an intuitively appealing framework for studying relationships
among interconnected brain mechanisms and their relevance to behaviour. As the space of …
among interconnected brain mechanisms and their relevance to behaviour. As the space of …
Predicting dynamic embedding trajectory in temporal interaction networks
S Kumar, X Zhang, J Leskovec - Proceedings of the 25th ACM SIGKDD …, 2019 - dl.acm.org
Modeling sequential interactions between users and items/products is crucial in domains
such as e-commerce, social networking, and education. Representation learning presents …
such as e-commerce, social networking, and education. Representation learning presents …
CFERE: Multi-type Chinese financial event relation extraction
Extracting various types of event relations in financial texts can benefit many downstream
applications supporting financial analysis. This paper addresses the multi-type event …
applications supporting financial analysis. This paper addresses the multi-type event …
Learning dynamic embeddings from temporal interactions
S Kumar, X Zhang, J Leskovec - arxiv preprint arxiv:1812.02289, 2018 - arxiv.org
Modeling a sequence of interactions between users and items (eg, products, posts, or
courses) is crucial in domains such as e-commerce, social networking, and education to …
courses) is crucial in domains such as e-commerce, social networking, and education to …
Extracting temporal and causal relations based on event networks
DT Vo, F Al-Obeidat, E Bagheri - Information Processing & Management, 2020 - Elsevier
Event relations specify how different event flows expressed within the context of a textual
passage relate to each other in terms of temporal and causal sequences. There have …
passage relate to each other in terms of temporal and causal sequences. There have …
Intensity profile projection: A framework for continuous-time representation learning for dynamic networks
A Modell, I Gallagher, E Ceccherini… - Advances in …, 2023 - proceedings.neurips.cc
We present a new representation learning framework, Intensity Profile Projection, for
continuous-time dynamic network data. Given triples $(i, j, t) $, each representing a time …
continuous-time dynamic network data. Given triples $(i, j, t) $, each representing a time …
CHIP: A Hawkes process model for continuous-time networks with scalable and consistent estimation
M Arastuie, S Paul, K Xu - Advances in neural information …, 2020 - proceedings.neurips.cc
In many application settings involving networks, such as messages between users of an on-
line social network or transactions between traders in financial markets, the observed data …
line social network or transactions between traders in financial markets, the observed data …
Continuous latent position models for instantaneous interactions
R Rastelli, M Corneli - Network Science, 2023 - cambridge.org
We create a framework to analyze the timing and frequency of instantaneous interactions
between pairs of entities. This type of interaction data is especially common nowadays and …
between pairs of entities. This type of interaction data is especially common nowadays and …
Direct embedding of temporal network edges via time-decayed line graphs
S Chanpuriya, RA Rossi, S Kim, T Yu… - arxiv preprint arxiv …, 2022 - arxiv.org
Temporal networks model a variety of important phenomena involving timed interactions
between entities. Existing methods for machine learning on temporal networks generally …
between entities. Existing methods for machine learning on temporal networks generally …
The multivariate community hawkes model for dependent relational events in continuous-time networks
H Soliman, L Zhao, Z Huang… - … on Machine Learning, 2022 - proceedings.mlr.press
The stochastic block model (SBM) is one of the most widely used generative models for
network data. Many continuous-time dynamic network models are built upon the same …
network data. Many continuous-time dynamic network models are built upon the same …