TY - JOUR
T1 - Learning dynamic embeddings for temporal attributed networks
AU - Luodi, Xie
AU - Tian, Hui
AU - Shen, Hong
N1 - Publisher Copyright:
© 2023
PY - 2024/2/28
Y1 - 2024/2/28
N2 - Learning low-dimensional representations for networks has attracted considerable research interest due to the ever-increasing volume and variety of network-based information. It becomes especially important and challenging for temporal networks whose nodes and attributes evolve as time changes. To address the problem of temporal network embedding, we propose a novel Dynamic Bayesian Temporal Network Embedding (DBTNE) method that adopts a probabilistic framework and represents network embeddings as Gaussian distributions for learning effective embeddings for temporal networks. Unlike the existing temporal network embedding methods that learn dynamic embeddings only for nodes, our DBTNE jointly learns the dynamic embeddings of both nodes and attributes in temporal attributed networks for effectively capturing the similarities among them, which greatly facilitates attribute-related learning tasks such as link prediction, object classification and user profiling. In addition, we model the temporal dynamics of both nodes and attributes with neural ordinary differential equations, which supports effective and efficient capture of long-range dependencies among embeddings at different timestamps. Extensive experimental results on real-world temporal networks demonstrate that our DBTNE achieves better performance than popular state-of-the-art methods, including DeepWalk, Node2Vec, CAN, HTNE, M2DNE, MTSN, Dane-ATT, for typical graph learning tasks, such as dynamic link prediction and node classification.
AB - Learning low-dimensional representations for networks has attracted considerable research interest due to the ever-increasing volume and variety of network-based information. It becomes especially important and challenging for temporal networks whose nodes and attributes evolve as time changes. To address the problem of temporal network embedding, we propose a novel Dynamic Bayesian Temporal Network Embedding (DBTNE) method that adopts a probabilistic framework and represents network embeddings as Gaussian distributions for learning effective embeddings for temporal networks. Unlike the existing temporal network embedding methods that learn dynamic embeddings only for nodes, our DBTNE jointly learns the dynamic embeddings of both nodes and attributes in temporal attributed networks for effectively capturing the similarities among them, which greatly facilitates attribute-related learning tasks such as link prediction, object classification and user profiling. In addition, we model the temporal dynamics of both nodes and attributes with neural ordinary differential equations, which supports effective and efficient capture of long-range dependencies among embeddings at different timestamps. Extensive experimental results on real-world temporal networks demonstrate that our DBTNE achieves better performance than popular state-of-the-art methods, including DeepWalk, Node2Vec, CAN, HTNE, M2DNE, MTSN, Dane-ATT, for typical graph learning tasks, such as dynamic link prediction and node classification.
KW - Bayesian inference
KW - Dynamic link prediction
KW - Node classification
KW - Temporal network embedding
KW - Variational inference
UR - http://www.scopus.com/inward/record.url?scp=85182600515&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2023.111308
DO - 10.1016/j.knosys.2023.111308
M3 - Article
AN - SCOPUS:85182600515
SN - 0950-7051
VL - 286
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 111308
ER -