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 …
EgoMUIL: Enhancing Spatio-temporal User Identity Linkage in Location-Based Social Networks with Ego-Mo Hypergraph
H Huang, F Ding, H Yin, G Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Users tend to own multiple accounts on different location-based social network (LBSN)
platforms, and they typically engage with diverse social circles on each platform within the …
platforms, and they typically engage with diverse social circles on each platform within the …
CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games
A Adesokan, R Kinney, EE Tsiropoulou - Future Internet, 2024 - mdpi.com
This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the
CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select …
CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select …
Leveraging Trustworthy Node Attributes for Effective Network Alignment
With the prevalence of social media platforms, accurately identifying the same users across
different networks through network alignment has become crucial. Existing methods often …
different networks through network alignment has become crucial. Existing methods often …
A Fine-Grained Regularization Scheme for Nonnegative Latent Factorization of High-Dimensional and Incomplete Tensors
Abstract A Dynamically Weighted Directed Network (DWDN) fundamentally illustrates the
complex interactions among massive nodes from a big-data-oriented application, like the …
complex interactions among massive nodes from a big-data-oriented application, like the …
Improving collaborative filtering with SNE–GCN: a second-order neighbor enhanced graph convolutional network
T Yan, L Cao, P Chai, S Yu - Multimedia Systems, 2024 - Springer
Graph collaborative filtering uses user-item interactions to capture user preferences for
items. While this approach proves highly effective, its performance may suffer from the …
items. While this approach proves highly effective, its performance may suffer from the …
Unsupervised Graph Structure Learning Based on Optimal Graph Topology Modeling and Adaptive Data Augmentation
Graph neural networks (GNNs) are effective for structured data analysis but face reduced
learning accuracy due to noisy connections and the necessity for explicit graph structures …
learning accuracy due to noisy connections and the necessity for explicit graph structures …
Surveys and Evaluation of Recent Network Alignment Methods
JH Lim, DH Seo, SW Kim - Proceedings of the Korea Information …, 2024 - koreascience.kr
최근 온라인 소셜 네트워크 플랫폼의 증가에 따라 사용자들은 다양한 서비스를 제공받기 위해
여러 소셜 네트워크 플랫폼에 가입하는 경향이 있다. 네트워크 정렬은 보안상의 문제로 …
여러 소셜 네트워크 플랫폼에 가입하는 경향이 있다. 네트워크 정렬은 보안상의 문제로 …