Line graph neural networks for link weight prediction
In real-world networks, predicting the weight (strength) of links is as crucial as predicting the
existence of the links themselves. Previous studies have primarily used shallow graph …
existence of the links themselves. Previous studies have primarily used shallow graph …
[HTML][HTML] LWP-WL: Link weight prediction based on CNNs and the Weisfeiler–Lehman algorithm
We present a new technique for link weight prediction, the Link Weight Prediction Weisfeiler–
Lehman (LWP-WL) method that learns from graph structure features and link relationship …
Lehman (LWP-WL) method that learns from graph structure features and link relationship …
Link weight prediction using supervised learning methods and its application to yelp layered network
Real-world networks feature weights of interactions, where link weights often represent
some physical attributes. In many situations, to recover the missing data or predict the …
some physical attributes. In many situations, to recover the missing data or predict the …
Self-attention enhanced auto-encoder for link weight prediction with graph compression
Predicting unobservable or missing weights over links on various real-world networks is of
fundamental scientific significance in disparate disciplines like sociology, biology, and …
fundamental scientific significance in disparate disciplines like sociology, biology, and …
Link prediction via graph attention network
Link prediction aims to infer missing links or predicting the future ones based on currently
observed partial networks, it is a fundamental problem in network science with tremendous …
observed partial networks, it is a fundamental problem in network science with tremendous …
Line graph neural networks for link prediction
We consider the graph link prediction task, which is a classic graph analytical problem with
many real-world applications. With the advances of deep learning, current link prediction …
many real-world applications. With the advances of deep learning, current link prediction …
A multi-scale approach for graph link prediction
Deep models can be made scale-invariant when trained with multi-scale information.
Images can be easily made multi-scale, given their grid-like structures. Extending this to …
Images can be easily made multi-scale, given their grid-like structures. Extending this to …
Graph attention network via node similarity for link prediction
K Yang, Y Liu, Z Zhao, X Zhou, P Ding - The European Physical Journal B, 2023 - Springer
Link prediction is a classic complex network analytical problem to predict the possible links
according to the known network structure information. Considering similar nodes should …
according to the known network structure information. Considering similar nodes should …
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction
A Chatterjee, R Walters, G Menichetti… - arXiv preprint arXiv …, 2023 - arxiv.org
Link prediction is a crucial task in graph machine learning with diverse applications. We
explore the interplay between node attributes and graph topology and demonstrate that …
explore the interplay between node attributes and graph topology and demonstrate that …
Deep link-prediction based on the local structure of bipartite networks
Link prediction based on bipartite networks can not only mine hidden relationships between
different types of nodes, but also reveal the inherent law of network evolution. Existing …
different types of nodes, but also reveal the inherent law of network evolution. Existing …