Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Francesco Sanna Passino"'
Autor:
Francesco Sanna Passino, Niall M. Adams, Edward A.K. Cohen, Marina Evangelou, Nicholas A. Heard
Publikováno v:
Harvard Data Science Review, Vol 5, Iss 1 (2023)
Externí odkaz:
https://doaj.org/article/7c3fd62e1a4d48a7be06ed9dfd8c38ee
A new class of models for dynamic networks is proposed, called mutually exciting point process graphs (MEG). MEG is a scalable network-wide statistical model for point processes with dyadic marks, which can be used for anomaly detection when assessin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ddedd4451dafb7358430d8a74a219a73
Publikováno v:
WWW
We consider the problem of predicting users’ preferences on online platforms. We build on recent findings suggesting that users’ preferences change over time, and that helping users expand their horizons is important in ensuring that they stay en
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b7a2c6ed003be77c8d7ed475717a9f52
https://doi.org/10.1145/3442381.3450028
https://doi.org/10.1145/3442381.3450028
Spectral embedding of network adjacency matrices often produces node representations living approximately around low-dimensional submanifold structures. In particular, hidden substructure is expected to arise when the graph is generated from a latent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a23975a0aa509422920b7bc76885f8d
Spectral clustering is a popular method for community detection in network graphs: starting from a matrix representation of the graph, the nodes are clustered on a low dimensional projection obtained from a truncated spectral decomposition of the mat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3fb4362223f2fd4043054b623bb33b7
http://arxiv.org/abs/2011.04558
http://arxiv.org/abs/2011.04558
Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f828fa54eeba37b4f833c18a97e720e
http://hdl.handle.net/10044/1/80141
http://hdl.handle.net/10044/1/80141
Periodic patterns can often be observed in real-world event time data, possibly mixed with non-periodic arrival times. For modelling purposes, it is necessary to correctly distinguish the two types of events. This task has particularly important impl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f57f32ed5a2f0fefc2c0b736c08f9ab
http://hdl.handle.net/10044/1/79250
http://hdl.handle.net/10044/1/79250
Graph link prediction is an important task in cyber-security: relationships between entities within a computer network, such as users interacting with computers, or system libraries and the corresponding processes that use them, can provide key insig
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c6d00a14bcda861c4b68ee537d2bdff
http://arxiv.org/abs/2001.09456
http://arxiv.org/abs/2001.09456
Dynamic interaction networks frequently arise in biology, communications technology and the social sciences, representing, for example, neuronal connectivity in the brain, internet connections between computers and human interactions within social ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a41308e7f0bbbc974f79d4b61884f3bc
http://hdl.handle.net/10044/1/73535
http://hdl.handle.net/10044/1/73535