Cyber threat intelligence mining for proactive cybersecurity defense: a survey and new perspectives
Today's cyber attacks have become more severe and frequent, which calls for a new line of
security defenses to protect against them. The dynamic nature of new-generation threats …
security defenses to protect against them. The dynamic nature of new-generation threats …
A comprehensive survey on community detection with deep learning
Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …
connections of a group of members that are different from those in other communities. The …
Collaborative representation learning for nodes and relations via heterogeneous graph neural network
Heterogeneous graphs, which consist of multiple types of nodes and edges, are highly
suitable for characterizing real-world complex systems. In recent years, due to their strong …
suitable for characterizing real-world complex systems. In recent years, due to their strong …
Normalizing flow-based neural process for few-shot knowledge graph completion
Knowledge graphs (KGs), as a structured form of knowledge representation, have been
widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC) …
widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC) …
SR-HGN: Semantic-and relation-aware heterogeneous graph neural network
Abstract Graph Neural Networks (GNNs) have received considerable attention in recent
years due to their unique ability to model both topologies and semantics in the graphs. In …
years due to their unique ability to model both topologies and semantics in the graphs. In …
A novel network core structure extraction algorithm utilized variational autoencoder for community detection
R Fei, Y Wan, B Hu, A Li, Q Li - Expert Systems with Applications, 2023 - Elsevier
Community detection technologies have the general research significance in complex
networks, in which the topology information of network is worthy to be the focus for its widely …
networks, in which the topology information of network is worthy to be the focus for its widely …
Integrating heterogeneous structures and community semantics for unsupervised community detection in heterogeneous networks
Y Zhao, W Li, F Liu, J Wang, AM Luvembe - Expert Systems with …, 2024 - Elsevier
Community detection aims to discover hidden communities or groups in complex networks
and is essentially unsupervised clustering behavior. However, most of the existing …
and is essentially unsupervised clustering behavior. However, most of the existing …
Heterogeneous graph neural network for privacy-preserving recommendation
Social networks are considered to be heterogeneous graph neural networks (HGNNs) with
deep learning technological advances. HGNNs, compared to homogeneous data, absorb …
deep learning technological advances. HGNNs, compared to homogeneous data, absorb …
Effective community search over large star-schema heterogeneous information networks
Community search (CS) enables personalized community discovery and has found a wide
spectrum of emerging applications such as setting up social events and friend …
spectrum of emerging applications such as setting up social events and friend …
Graph sequential neural ode process for link prediction on dynamic and sparse graphs
Link prediction on dynamic graphs is an important task in graph mining. Existing approaches
based on dynamic graph neural networks (DGNNs) typically require a significant amount of …
based on dynamic graph neural networks (DGNNs) typically require a significant amount of …