Reconciling competing sampling strategies of network embedding
Network embedding plays a significant role in a variety of applications. To capture the
topology of the network, most of the existing network embedding algorithms follow a …
topology of the network, most of the existing network embedding algorithms follow a …
From trainable negative depth to edge heterophily in graphs
Finding the proper depth $ d $ of a graph convolutional network (GCN) that provides strong
representation ability has drawn significant attention, yet nonetheless largely remains an …
representation ability has drawn significant attention, yet nonetheless largely remains an …
Hierarchical multi-marginal optimal transport for network alignment
Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …
essential prerequisite for joint learning on multiple networks. Despite great success in …
Pacer: Network embedding from positional to structural
Network embedding plays an important role in a variety of social network applications.
Existing network embedding methods, explicitly or implicitly, can be categorized into …
Existing network embedding methods, explicitly or implicitly, can be categorized into …
Sterling: Synergistic representation learning on bipartite graphs
A fundamental challenge of bipartite graph representation learning is how to extract
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …
MentorGNN: Deriving Curriculum for Pre-Training GNNs
Graph pre-training strategies have been attracting a surge of attention in the graph mining
community, due to their flexibility in parameterizing graph neural networks (GNNs) without …
community, due to their flexibility in parameterizing graph neural networks (GNNs) without …
X-GOAL: Multiplex heterogeneous graph prototypical contrastive learning
Graphs are powerful representations for relations among objects, which have attracted
plenty of attention in both academia and industry. A fundamental challenge for graph …
plenty of attention in both academia and industry. A fundamental challenge for graph …
DPPIN: A biological repository of dynamic protein-protein interaction network data
In the big data era, the relationship between entries becomes more and more complex.
Many graph (or network) algorithms have already paid attention to dynamic networks, which …
Many graph (or network) algorithms have already paid attention to dynamic networks, which …
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
Graph-based collaborative filtering has emerged as a powerful paradigm for delivering
personalized recommendations. Despite their demonstrated effectiveness, these methods …
personalized recommendations. Despite their demonstrated effectiveness, these methods …
ArieL: Adversarial Graph Contrastive Learning
Contrastive learning is an effective unsupervised method in graph representation learning.
The key component of contrastive learning lies in the construction of positive and negative …
The key component of contrastive learning lies in the construction of positive and negative …