A hybrid approach for categorizing images based on complex networks and neural networks

There are several methods for categorizing images, the most of which are statistical, geometric, model-based and structural methods. In this paper, a new method for describing images based on complex network models is presented. Each image contains a number of key points that can be identified through standard edge detection algorithms. To understand each image better, we can use these points to create a graph of the image. In order to facilitate the use of graphs, generated graphs are created in the form of a complex network of small-worlds. Complex grid features such as topological and dynamic features can be used to display image-related features. After generating this information, it normalizes them and uses them as suitable features for categorizing images. For this purpose, the generated information is given to the neural network. Based on these features and the use of neural networks, comparisons between new images are performed. The results of the article show that this method has a good performance in identifying similarities and finally categorizing them.

Studies on the Decrease Mechanisms of Typical Complex Networks

Network decrease caused by the removal of nodes is an important evolution process that is paralleled with network growth. However, many complex network models usually lacked a sound decrease mechanism. Thus, they failed to capture how to cope with decreases in real life. The paper proposed decrease mechanisms for three typical types of networks, including the ER networks, the WS small-world networks and the BA scale-free networks. The proposed mechanisms maintained their key features in continuous and independent decrease processes, such as the random connections of ER networks, the long-range connections based on nearest-coupled network of WS networks and the tendency connections and the scale-free feature of BA networks. Experimental results showed that these mechanisms also maintained other topology characteristics including the degree distribution, clustering coefficient, average length of shortest-paths and diameter during decreases. Our studies also showed that it was quite difficult to find an efficient decrease mechanism for BA networks to withstand the continuous attacks at the high-degree nodes, because of the unequal status of nodes.

Impact of Network Topology on the Spread of Infectious Diseases

The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.

Abstract 313: Visualizing Out-Of-Hospital Cardiac Arrest in the Young: A Network Analysis of the Cardiac Arrest Registry for Enhanced Survival in Chicago

Introduction: The interactions of various variables on out-of-hospital cardiac arrest (OHCA) in the young (1-35 years old) outcomes are complex. Network models have emerged as a way to abstract complex systems and gain insights into relational patterns among observed variables. Hypothesis: Network analysis helps provide qualitative and quantitative insights into how various variables interact with each other and affect outcomes in OHCA in the young. Methods: A mixed graphical network analysis was performed using variables collected by CARES. The network allows the visualization and quantification of each unique interaction between two variables that cannot be explained away by other variables in the data set. The strength of the underlying interaction is proportional to the thickness of the connections (edges) between the variables (nodes). We used the mgm package in R. Results: Figure 1 shows the network of the OHCA in the young cases in Chicago from 2013 to 2017. There are apparent clusters. Sustained return of spontaneous circulation and hypothermia are strongly correlated with survival and neurological outcomes. This cluster is in turn connected to the rest of the network by survival to emergency room. The interaction between any two variables can also be quantified. For example, American Indians cases occur more often in disadvantaged locations when compared to Whites (OR 4.5). The network also predicts how much one node can be explained by adjacent nodes. Only 20% of survival to emergency room is explained by its adjacent nodes. The remaining 80% is attributed to variables not represented in this network. This suggests that interventions to improve this node is difficult unless further data is available. Conclusion: Network analysis provides both a qualitative and quantitative evaluation of the complex system governing OHCA in the young. The networks predictive capability could help in identifying the most effective interventions to improve outcomes.

A New Evaluation Method of Node Importance in Directed Weighted Complex Networks

Current researches on node importance evaluation mainly focus on undirected and unweighted networks, which fail to reflect the real world in a comprehensive and objective way. Based on directed weighted complex network models, the paper introduces the concept of in-weight intensity of nodes and thereby presents a new method to identify key nodes by using an importance evaluation matrix. The method not only considers the direction and weight of edges, but also takes into account the position importance of nodes and the importance contributions of adjacent nodes. Finally, the paper applies the algorithm to a microblog-forwarding network composed of 34 users, then compares the evaluation results with traditional methods. The experiment shows that the method proposed can effectively evaluate the node importance in directed weighted networks.

Small-World and Scale-Free Network Models for IoT Systems

It is expected that Internet of Things (IoT) revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.