Detection Models and Computation for Understanding the Frangibility of Complex Networks [Complexity]
https://review.hindawi.com/submit?journal=complexity

Call for papers
This Issue is now open for submissions.
Submission deadline
04 Dec 2020

Description
A network is one of the best data models to represent human behavior and interactions. For instance, social network users can share their interests and news on social network platforms, traffic users can express their common interests and daily activities by trajectory networks, and telephone network users show their social interactions at periodic levels. In the area of network science, lots of research has been developed to investigate information flow and influence diffusion models in such complex networks. However, the frangibility of the network has not yet been fully explored. The frangibility of a network may be affected by multiple factors in the network, e.g., the removal of users may result in the disconnection of the network, or the high communication cost of the network; the addition of users may enrich the network so that the communication cost can be largely reduced; the changed paths of consuming network content may also vary the communication of the information flow in the network.

It is highly challenging to diagnose the frangibility of complex networks. Traditional works have paid great efforts to discover the influential users or seed users at different situations using metrics such as centrality, betweenness, and ego network, or using the linear threshold model, independent cascade model, and the weighted user-defined path models. However, these works still focused on the influence diffusion, rather than the frangibility of the networks in the dynamic environment. In addition, it is desirable for researchers to investigate the non-human defined metrics to uncover the frangibility of networks, with which the correlated key players can be retrieved. To solve this challenging issue, in machine learning, concept learning has become one of the primary research tools in small sample learning research. The concept learning strategy aims to perform recognition or form new concepts from a few observations though fast processing. Concept learning employs matching rules to associate the concepts in the concept system with small sample input. It is very helpful to perform cognition or complete a recognition task in data analytics. With the capability of small samples and the learned knowledge, it can help to advance the realistic key player discovery models and efficiently find the key players for different target criteria.

This Special Issue invites researchers working in the field of knowledge-based systems, data science, and artificial intelligence to submit original papers discussing and promoting ideas and practices about advanced complex network management and analytics technologies for the frangibility-driven key user discovery. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

Key user detection model with dynamic network change
Concept learning from small samples to user profiling in networks
Concept learning from small samples to attribute filling in networks
Concept learning for event type disambiguation in networks
New knowledge-driven concept learning across multiple networks
Root cause diagnoses-based concept learning for network flow change
Novel feature detection model for identifying the frangibility of networks
Efficient structural hole computation in complex networks
Top-k influential user detection in attributed networks
Anchor vertex exploration to enrich the networks
Dynamic network metric evaluation
Community-level information diffusion with concept learning