HCNA: Hyperbolic contrastive learning framework for self-supervised network alignment
Network alignment, or identifying the same entities (anchors) across multiple networks, has
significant applications across diverse fields. Unsupervised approaches for network …
significant applications across diverse fields. Unsupervised approaches for network …
Towards higher-order topological consistency for unsupervised network alignment
Network alignment task, which aims to identify corresponding nodes in different networks, is
of great significance for many subsequent applications. Without the need for labeled anchor …
of great significance for many subsequent applications. Without the need for labeled anchor …
Structural representation learning for network alignment with self-supervised anchor links
Network alignment, the problem of identifying similar nodes across networks, is an emerging
research topic due to its ubiquitous applications in many data domains such as social …
research topic due to its ubiquitous applications in many data domains such as social …
Network Alignment enhanced via modeling heterogeneity of anchor nodes
Network Alignment (NA), which aims to find the nodes that represent the same entity (ie,
anchor nodes) across different networks, is a fundamental problem in many cross-network …
anchor nodes) across different networks, is a fundamental problem in many cross-network …
HackGAN: Harmonious cross-network mapping using CycleGAN with Wasserstein–Procrustes learning for unsupervised network alignment
L Yang, X Wang, J Zhang, J Yang, Y Xu… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Network alignment (NA) that identifies equivalent nodes across networks is an effective tool
for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter …
for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter …
Balancing consistency and disparity in network alignment
Network alignment plays an important role in a variety of applications. Many traditional
methods explicitly or implicitly assume the alignment consistency which might suffer from …
methods explicitly or implicitly assume the alignment consistency which might suffer from …
Collaborative cross-network embedding framework for network alignment
Network alignment aims to identify the corresponding nodes belonging to the same entity
across different networks, which is a fundamental task in various applications. Existing …
across different networks, which is a fundamental task in various applications. Existing …
Unsupervised multiple network alignment with multinominal gan and variational inference
Network alignment techniques, which aim to identify the same entities across multiple
networks, often suffer challenges from feature inconsistency to transitivity law preservation …
networks, often suffer challenges from feature inconsistency to transitivity law preservation …
Dual adversarial learning based network alignment
Network alignment, which aims to learn a matching between the same entities across
multiple information networks, often suffers challenges from feature inconsistency, high …
multiple information networks, often suffers challenges from feature inconsistency, high …
Deep adversarial network alignment
Network alignment, in general, seeks to discover the hidden underlying correspondence
between nodes across two (or more) networks when given their network structure. However …
between nodes across two (or more) networks when given their network structure. However …