Link prediction techniques, applications, and performance: A survey
Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …
Auto-encoders in deep learning—a review with new perspectives
S Chen, W Guo - Mathematics, 2023 - mdpi.com
Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …
development of neural networks. The auto-encoder is a key component of deep structure …
Predicting dynamic embedding trajectory in temporal interaction networks
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 …
Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey
Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …
recommender systems and epidemiology. Representing complex networks as structures …
node2vec: Scalable feature learning for networks
Prediction tasks over nodes and edges in networks require careful effort in engineering
features used by learning algorithms. Recent research in the broader field of representation …
features used by learning algorithms. Recent research in the broader field of representation …
Predicting multicellular function through multi-layer tissue networks
Motivation Understanding functions of proteins in specific human tissues is essential for
insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular …
insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular …
GC-LSTM: Graph convolution embedded LSTM for dynamic network link prediction
J Chen, X Wang, X Xu - Applied Intelligence, 2022 - Springer
Dynamic network link prediction is becoming a hot topic in network science, due to its wide
applications in biology, sociology, economy and industry. However, it is a challenge since …
applications in biology, sociology, economy and industry. However, it is a challenge since …
Link prediction in social networks: the state-of-the-art
In social networks, link prediction predicts missing links in current networks and new or
dissolution links in future networks, is important for mining and analyzing the evolution of …
dissolution links in future networks, is important for mining and analyzing the evolution of …
GCN-GAN: A non-linear temporal link prediction model for weighted dynamic networks
In this paper, we generally formulate the dynamics prediction problem of various network
systems (eg, the prediction of mobility, traffic and topology) as the temporal link prediction …
systems (eg, the prediction of mobility, traffic and topology) as the temporal link prediction …
E-LSTM-D: A deep learning framework for dynamic network link prediction
J Chen, J Zhang, X Xu, C Fu, D Zhang… - … on Systems, Man …, 2019 - ieeexplore.ieee.org
Predicting the potential relations between nodes in networks, known as link prediction, has
long been a challenge in network science. However, most studies just focused on link …
long been a challenge in network science. However, most studies just focused on link …