Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy

An Evaluation Model for Node Influence Based on Heuristic Spatiotemporal Features

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks.

Exploring the mechanism of Erteng-Sanjie capsule in treating gastric and colorectal cancers via network pharmacology and in-vivo validation

ETHNOPHARMACOLOGICAL RELEVANCE

The Erteng-Sanjie capsule (ETSJC) has therapeutic effects against gastric cancer (GC) and colorectal cancer (CRC). However, its underlying pharmacological mechanism remains unclear.

AIM OF THE STUDY

To explore the pharmacological mechanism of ETSJC against GC and CRC via network pharmacology and in-vivo validation.

MATERIALS AND METHODS

Data on the ingredients of ETSJC were obtained from the TCMSP and HERB databases. Further, details on the related targets of the active ingredients were collected from the HERB and SwissTargetPrediction databases. The targets in GC and CRC, which were screened from the OMIM, GeneCards, and TTD databases, were uploaded to STRING for a separate protein-protein interaction network analysis. The common targets shared by ETSJC, GC, and CRC were then screened. Cytoscape and STRING were used to construct the networks of herbs-compounds-targets and PPI. Metascape was utilized to analyze the enrichment of the GO and KEGG pathways. Molecular docking was used to validate the potential binding mode between the core ingredients and targets. Finally, the predicted results were verified with animal experiment.

RESULTS

Eight core ingredients (resveratrol, quercetin, luteolin, baicalein, delphinidin, kaempferol, pinocembrin, and naringenin) and six core targets (TP53, SRC, PIK3R1, AKT1, MAPK3, and STAT3) were filtered via network analysis. The molecular mechanism mainly involved the positive regulation of various processes such as cell migration, protein phosphorylation, and the PI3K-Akt signaling pathway. Molecular docking revealed that the core ingredients could be significantly combined with all core targets. The animal experiment revealed that ETSJC could suppress proliferation and promote apoptosis of both GC and CRC tumor cells by regulating the PI3K/Akt signaling pathway.

CONCLUSIONS

Multiple targets (TP53, SRC, AKT1, and STAT3) were important in GC and CRC. ETSJC could act on these targets and engage in different pathways against GC and CRC. Simultaneously, inhibiting the PI3K/Akt signaling pathway was a promising therapeutic mechanism for treating GC and CRC.

Finding influential nodes in complex networks based on Kullback-Leibler model within the neighborhood

As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback-Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales.

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu

Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus-subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric's performance in important node identification is greatly improved, and it demonstrates good network resilience.

The use of Xuanbai Chengqi decoction on monkeypox disease through the estrone-target AR interaction

Introduction

After COVID-19, there was an outbreak of a new infectious disease caused by monkeypox virus. So far, no specific drug has been found to treat it. Xuanbai Chengqi decoction (XBCQD) has shown effects against a variety of viruses in China.

Methods

We searched for the active compounds and potential targets for XBCQD from multiple open databases and literature. Monkeypox related targets were searched out from the OMIM and GeneCards databases. After determining the assumed targets of XBCQD for monkeypox treatment, we built the PPI network and used R for GO enrichment and KEGG pathway analysis. The interactions between the active compounds and the hub targets were investigated by molecular docking and molecular dynamics (MD) simulations.

Results

In total, 5 active compounds and 10 hub targets of XBCQD were screened out. GO enrichment and KEGG analysis demonstrated that XBCQD plays a therapeutic role in monkeypox mainly by regulating signaling pathways related to viral infection and inflammatory response. The main active compound estrone binding to target AR was confirmed to be the best therapy choice for monkeypox.

Discussion

This study systematically explored the interactions between the bioactive compounds of XBCQD and the monkeypox-specific XBCQD targets using network pharmacological methods, bioinformatics analyses and molecular simulations, suggesting that XBCQD could have a beneficial therapeutic effect on monkeypox by reducing the inflammatory damage and viral replication via multiple pathways. The use of XBCQD on monkeypox disease was confirmed to be best worked through the estrone-target AR interaction. Our work could provide evidence and guidance for further research on the treatment of monkeypox disease.

The Structure Entropy-Based Node Importance Ranking Method for Graph Data

Due to its wide application across many disciplines, how to make an efficient ranking for nodes in graph data has become an urgent topic. It is well-known that most classical methods only consider the local structure information of nodes, but ignore the global structure information of graph data. In order to further explore the influence of structure information on node importance, this paper designs a structure entropy-based node importance ranking method. Firstly, the target node and its associated edges are removed from the initial graph data. Next, the structure entropy of graph data can be constructed by considering the local and global structure information at the same time, in which case all nodes can be ranked. The effectiveness of the proposed method was tested by comparing it with five benchmark methods. The experimental results show that the structure entropy-based node importance ranking method performs well on eight real-world datasets.

Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength

Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes. Graph neural networks (GNNs) have emerged as a prominent deep learning architecture, capable of efficiently aggregating node information and discerning node influence. However, existing graph neural networks often ignore the strength of the relationships between nodes when aggregating information about neighboring nodes. In complex networks, neighboring nodes often do not have the same influence on the target node, so the existing graph neural network methods are not effective. In addition, the diversity of complex networks also makes it difficult to adapt node features with a single attribute to different types of networks. To address the above problems, the paper constructs node input features using information entropy combined with the node degree value and the average degree of the neighbor, and proposes a simple and effective graph neural network model. The model obtains the strength of the relationships between nodes by considering the degree of neighborhood overlap, and uses this as the basis for message passing, thereby effectively aggregating information about nodes and their neighborhoods. Experiments are conducted on 12 real networks, using the SIR model to verify the effectiveness of the model with the benchmark method. The experimental results show that the model can identify the influence of nodes in complex networks more effectively.

Local-Forest Method for Superspreaders Identification in Online Social Networks

Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we first adopt the Push-Republish (PR) model as the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly decreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods.