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pro vyhledávání: '"Ghalebi, Elahe"'
Graph convolutional networks (GCNs) allow us to learn topologically-aware node embeddings, which can be useful for classification or link prediction. However, they are unable to capture long-range dependencies between nodes without adding additional
Externí odkaz:
http://arxiv.org/abs/2201.12670
In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), making their evaluation challenging. Currently, the standard process for
Externí odkaz:
http://arxiv.org/abs/2201.09871
Generative models are now used to create a variety of high-quality digital artifacts. Yet their use in designing physical objects has received far less attention. In this paper, we advocate for the construction toy, LEGO, as a platform for developing
Externí odkaz:
http://arxiv.org/abs/2012.11543
As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure that underpins these interactions. Often these interactions form a multi
Externí odkaz:
http://arxiv.org/abs/1910.05098
Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner. Such behavior can be well-captured by structured, nonparametric edge
Externí odkaz:
http://arxiv.org/abs/1905.11724
Can evolving networks be inferred and modeled without directly observing their nodes and edges? In many applications, the edges of a dynamic network might not be observed, but one can observe the dynamics of stochastic cascading processes (e.g., info
Externí odkaz:
http://arxiv.org/abs/1805.10616
Autor:
Ghalebi, Elahe
An important problem that arises in many applications of large networks is using observational data to infer the interactions (i.e., edges) between individuals (or vertices) which leads to complex collective behavior. In our work, we focus on time-ev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::067b2cef39a653a217ffb941b5985310
Akademický článek
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Autor:
Mahyar, Hamidreza, Hasheminezhad, Rouzbeh, Ghalebi, Elahe, Nazemian, Ali, Grosu, Radu, Movaghar, Ali, Rabiee, Hamid
Publikováno v:
Social Network Analysis and Mining; December 2018, Vol. 8 Issue: 1 p1-24, 24p
