Adversarial Deep Network Embedding for Cross-Network Node Classification

In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.

Methodologies Utilized and Lessons Learned in High Threat Environments and Mass Casualty Environments.

In May 2019, the author was awarded the Australian Tactical Medical Association (ATMA) study grant to attend the Special Operations Medicine Scientific Assembly (SOMSA) 2019 in Charlotte, North Carolina in the United States of America. Whilst in the U.S. the author had the opportunity to hear many talks, attend labs and talk to many first responders in high threat and austere environments to learn about how they operate, and the lessons learned from their experiences. This report highlights the two objectives of the study grant: Record the key content and lessons learned by attendance at SOMSA 2019. Discuss techniques utilized and lessons learned by first responders operating in high threat environments and mass casualty incidents. The SOMSA brings together many like-minded pre-hospital, tactical, wilderness, austere, disaster and deployed medicine operators from all around the world to share their learnings with a primary goal to advance the art and science of special operations medical care. It is a great opportunity for military and civilian providers to learn, network and engage with industry partners showcasing innovative products and technology.

Proceedings of NetSciEd 2018

NetSciEd 2018: The NetSci Satellite Symposium on Network Science and Education was held as a full-day satellite symposium at NetSci 2018 in Paris, France, on 12 June 2018. This edition followed the previous NetSciEd satellites held in 2012--2017. NetSciEd is an excellent venue to discuss all topics related to network science and education, including educational activities to teach/learn network science as well as applications of network science to understand, model, and improve educational systems and practices. This booklet gathers the abstracts of the presentations from NetSciEd 2018. The authors have enriched them with external links and references of potential relevance to any readers who want to become involved in network science and education.

AGU Webinars: New Ways to Learn, Network, and Share Information

AGU Webinars provide the AGU community with timely and accessible information about a variety of relevant topics.

Bayesian Network with Association Rules Applied in the Recognition of Handwritten Digits

Classification Based on Association (CBA) algorithm built a classifier based on the association rules, but without considering the uncertainty in the classification problem. This paper proposed a Bayesian network classifier based on the association rules. The algorithm extracts the candidate set uses association rules and classification algorithms related to the network, then uses “greedy hill-climbing algorithm” to learn network structure to get a better topology, and verify that this algorithm is valid on handwritten numeral recognition.