Link prediction in complex networks: A survey
L Lü, T Zhou - Physica A: statistical mechanics and its applications, 2011 - Elsevier
Link prediction in complex networks has attracted increasing attention from both physical
and computer science communities. The algorithms can be used to extract missing …
and computer science communities. The algorithms can be used to extract missing …
Towards robust graph neural networks for noisy graphs with sparse labels
Graph Neural Networks (GNNs) have shown their great ability in modeling graph structured
data. However, real-world graphs usually contain structure noises and have limited labeled …
data. However, real-world graphs usually contain structure noises and have limited labeled …
Deepwalk: Online learning of social representations
We present DeepWalk, a novel approach for learning latent representations of vertices in a
network. These latent representations encode social relations in a continuous vector space …
network. These latent representations encode social relations in a continuous vector space …
Graph based anomaly detection and description: a survey
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …
such as security, finance, health care, and law enforcement. While numerous techniques …
A modified DeepWalk method for link prediction in attributed social network
The increasing growth of online social networks has drawn researchers' attention to link
prediction and has been adopted in many fields, including computer sciences, information …
prediction and has been adopted in many fields, including computer sciences, information …
Relational learning via latent social dimensions
Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather
than classical IID distribution. To address the interdependency among data instances …
than classical IID distribution. To address the interdependency among data instances …
Propagation kernels: efficient graph kernels from propagated information
We introduce propagation kernels, a general graph-kernel framework for efficiently
measuring the similarity of structured data. Propagation kernels are based on monitoring …
measuring the similarity of structured data. Propagation kernels are based on monitoring …
Rolx: structural role extraction & mining in large graphs
K Henderson, B Gallagher, T Eliassi-Rad… - Proceedings of the 18th …, 2012 - dl.acm.org
Given a network, intuitively two nodes belong to the same role if they have similar structural
behavior. Roles should be automatically determined from the data, and could be, for …
behavior. Roles should be automatically determined from the data, and could be, for …
Link prediction based on local random walk
W Liu, L Lü - Europhysics Letters, 2010 - iopscience.iop.org
The problem of missing link prediction in complex networks has attracted much attention
recently. Two difficulties in link prediction are the sparsity and huge size of the target …
recently. Two difficulties in link prediction are the sparsity and huge size of the target …