Community detection in node-attributed social networks: a survey
P Chunaev - Computer Science Review, 2020 - Elsevier
Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …
A survey of community search over big graphs
With the rapid development of information technologies, various big graphs are prevalent in
many real applications (eg, social media and knowledge bases). An important component of …
many real applications (eg, social media and knowledge bases). An important component of …
Fast federated machine unlearning with nonlinear functional theory
Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …
training data upon request from a trained federated learning model. Despite achieving …
A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix
The most basic and significant issue in complex network analysis is community detection,
which is a branch of machine learning. Most current community detection approaches, only …
which is a branch of machine learning. Most current community detection approaches, only …
Attribute truss community search
Recently, community search over graphs has attracted significant attention and many
algorithms have been developed for finding dense subgraphs from large graphs that contain …
algorithms have been developed for finding dense subgraphs from large graphs that contain …
Expressive 1-lipschitz neural networks for robust multiple graph learning against adversarial attacks
Recent findings have shown multiple graph learning models, such as graph classification
and graph matching, are highly vulnerable to adversarial attacks, ie small input …
and graph matching, are highly vulnerable to adversarial attacks, ie small input …
Adversarial attacks on deep graph matching
Despite achieving remarkable performance, deep graph learning models, such as node
classification and network embedding, suffer from harassment caused by small adversarial …
classification and network embedding, suffer from harassment caused by small adversarial …
Community search over big graphs: Models, algorithms, and opportunities
Communities serve as basic structures for understanding the organization of many real-
world networks, such as social, biological, collaboration, and communication networks …
world networks, such as social, biological, collaboration, and communication networks …
Integrated defense for resilient graph matching
A recent study has shown that graph matching models are vulnerable to adversarial
manipulation of their input which is intended to cause a mismatching. Nevertheless, there is …
manipulation of their input which is intended to cause a mismatching. Nevertheless, there is …
Unsupervised adversarial network alignment with reinforcement learning
Network alignment, which aims at learning 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 …