DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.

Adaptive transmission power level algorithm according to random deployment of nodes in WSN: خوارزمية سوية طاقة الإرسال المتكيفة حسب النشر العشوائي للعقد في شبكات الحساسات اللاسلكية

Wireless sensor nodes are generally deployed randomly in hostile, harsh and inaccessible environments. For this reason, the sensor nodes are supposed to operate over long periods of time without human intervention in order to extend the life of the network as much as possible, and also, it is not possible to restore the nodes or change their positions after their deployment, but by changing the transmitting power level and redeploying a new nodes above the deployment Previously, the network performance improves and we guarantee that the deployed nodes are not lost, and we also guarantee the operation of the network as a whole. The researcher has developed an algorithm "Adaptive transmission power level according to random deployment (ATPLRD)", where the presented algorithm includes determining the power levels relative to random deployment and identifying possible paths in the network in order to reach high interconnection between nodes to achieve the least number of published nodes at the lowest energy levels for the nodes, and also determines the most important nodes in the network whose exit or failure leads to the collapse of the network, and determining The boundary nodes of the network, as well as the weakest coverage areas, which represent gaps in the network, and from it determines the number of nodes needed to deploy within these gaps as few as possible. The results of the study showed that the imposed algorithm is effective in all of the above, and we focus in this research on adaptively determining the transmission energy levels of the nodes and reducing the number of deployed nodes that make the network work effectively and improving the quality of deployment by deploying additional nodes within the Reigon of Interest. The results showed achieving the least number of deployed nodes at the lowest transmission power level and achieving high interconnection between nodes. An overall energy consumption improvement of 31.25% was achieved.

Node Importance Based Protection in Power-Grid Optical Backbone Communication Networks

Power-grid optical backbone communication network is a special communication network serving for power system. With the development of new internet technology, there are more and more services carried by power-grid optical backbone communication networks. It plays an important role in the protection of nodes, especially important nodes which often carry important information of the network, when the network is under heavy traffic load. Hench, to solve this problem, we propose the concept of node importance and design a node importance-based protection algorithm under heavy traffic load scenario in power-grid optical backbone communication networks. Simulation results show that the proposed node importance based protection algorithm can obviously reduce blocking probability of the important nodes and improve the performance of the entire network in terms of blocking probability.

Rheum tanguticum Alleviates Cognitive Impairment in APP/PS1 Mice by Regulating Drug-Responsive Bacteria and Their Corresponding Microbial Metabolites

Drugs targeting intestinal bacteria have shown great efficacy for alleviating symptoms of Alzheimer’s disease (AD), and microbial metabolites are important messengers. Our previous work indicated that Rheum tanguticum effectively improved cognitive function and reshaped the gut microbial homeostasis in AD rats. However, its therapeutic mechanisms remain unclear. Herein, this study aimed to elaborate the mechanisms of rhubarb for the treatment of AD by identifying effective metabolites associated with rhubarb-responsive bacteria. The results found that rhubarb reduced hippocampal inflammation and neuronal damage in APP/PS1 transgenic (Tg) mice. 16S rRNA sequencing and metabolomic analysis revealed that gut microbiota and their metabolism in Tg mice were disturbed in an age-dependent manner. Rhubarb-responsive bacteria were further identified by real-time polymerase chain reaction (RT-PCR) sequencing. Four different metabolites reversed by rhubarb were found in the position of the important nodes on rhubarb-responsive bacteria and their corresponding metabolites combined with pathological indicators co-network. Furthermore, in vitro experiments demonstrated o-tyrosine not only inhibited the viabilities of primary neurons as well as BV-2 cells, but also increased the levels of intracellular reactive oxygen species and nitric oxide. In the end, the results suggest that rhubarb ameliorates cognitive impairment in Tg mice through decreasing the abundance of o-tyrosine in the gut owing to the regulation of rhubarb-responsive bacteria. Our study provides a promising strategy for elaborating therapeutic mechanisms of bacteria-targeted drugs for AD.

Identifying important nodes from content-associated heterogeneous graph by LeaderRank

Most real-world systems consist of a large number of interacting entities of many types. However, most of the current researches on systems are based on the assumption that the type of node or link in the network is unique. In other words, the network is homogeneous, containing the same type of nodes and links. Based on this assumption, differential information between nodes and edges is ignored. This paper firstly introduces the research background, challenges and significance of this research. Secondly, the basic concepts of the model are introduced. Thirdly, a novel type-sensitive LeaderRank algorithm is proposed and combined with distance rule to solve the importance ranking problem of content-associated heterogeneous graph nodes. Finally, the writer influence data set is used for experimental analysis to further prove the validity of the model.

MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding

Heterogeneous graph embedding has become a hot topic in network embedding in recent years and has been widely used in lots of practical scenarios. However, most of the existing heterogeneous graph embedding methods cannot make full use of all the auxiliary information. So we proposed a new method called Multi-Subgraph based Graph Convolution Network (MSGCN), which uses topology information, semantic information, and node feature information to learn node embedding vector. In MSGCN, the graph is firstly decomposed into multiple subgraphs according to the type of edges. Then convolution operation is adopted for each subgraph to obtain the node representations of each subgraph. Finally, the node representations are obtained by aggregating the representation vectors of nodes in each subgraph. Furthermore, we discussed the application of MSGCN with respect to a transductive learning task and inductive learning task, respectively. A node sampling method for inductive learning tasks to obtain representations of new nodes is proposed. This sampling method uses the attention mechanism to find important nodes and then assigns different weights to different nodes during aggregation. We conducted an experiment on three datasets. The experimental results indicate that our MSGCN outperforms the state-of-the-art methods in multi-class node classification tasks.

Post-COVID-19 Scenarios of Global Airline Traffic until 2040 That Reflect Airport Capacity Constraints and Mitigation Strategies

Although there has been an unprecedented decline in traffic volume due to the COVID-19 crisis, robust growth in global demand for air transport services in the past means that air traffic is expected to recover in the long term. While capacity constraints are currently not a major topic at airports due to the extremely low levels of traffic, there is growing evidence to suggest that important nodes of the worldwide airport network will struggle to deal with capacity constraints after the recovery. The objectives of this research were therefore as follows: to elaborate long-term global passenger and flight volume scenarios in a post-COVID-19 world; to conduct an empirical and model-based analysis of the impact of limited airport capacity on the future development of air traffic in these scenarios; and to derive general strategies for mitigating capacity constraints at certain international airports. Thus, the main aim of this paper is to present a model-based scenario analysis of the long-term impact of the COVID-19 crisis on the capacity situation for airports. Our results indicate that once the pandemic is over, the capacity crunch will remain on the airports’ agenda for some time.

Network Analysis Based on Important Node Selection and Community Detection

The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can be used to determine clusters of nodes based on the network structure. This paper shows by empirical means that node importance can be evaluated by a dual perspective—by combining the traditional centrality measures regarding the whole network as one unit, and by analyzing the node clusters yielded by community detection. Not only do these approaches offer overlapping results but also complementary information regarding the top important nodes. To confirm this mechanism, we performed experiments for synthetic and real-world networks and the results indicate the interesting relation between important nodes on community and network level.