Task Analysis

Enhancing Network Alignment through Multi-Scale Information Fusion
The network alignment task aims to identify the correspondence of the same nodes across networks, which plays a crucial role in many …
Cite DOI URL
Generative Evolutionary Anomaly Detection in Dynamic Networks
Anomaly detection in dynamic networks aims to find network elements (e.g., nodes, edges, subgraphs, change points) with significantly …
Cite DOI URL
Exploring Temporal Community Structure via Network Embedding
Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real …
Cite DOI URL
HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node Level
A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies …
Cite DOI URL
Representation Learning on Heterostructures via Heterogeneous Anonymous Walks
Capturing structural similarity has been a hot topic in the field of network embedding (NE) recently due to its great help in …
Cite DOI URL
Role Discovery-Guided Network Embedding Based on Autoencoder and Attention Mechanism
Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the …
Cite DOI URL
Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism
Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different …
Cite DOI URL
Green Parallel Online Offloading for DSCI-Type Tasks in IoT-Edge Systems
In order to meet people’s demands for intelligent and user-friendly Internet of Things (IoT) services, the amount of computation is …
Cite DOI URL
Inductive Link Prediction via Interactive Learning Across Relations in Multiplex Networks
Network embedding is an important class of link prediction methods, which can use the distance between learned low-dimensional node …
Cite DOI URL
DMRO: A Deep Meta Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing
With the explosive growth of mobile data and the unprecedented demand for computing power, resource-constrained edge devices cannot …
Cite DOI URL