Using Complex Networks for Mining Malicious Activities in a Collaborative Map

Bipartite networks that characterize complex relationships among data arise in various domains. The existing bipartite network models are mainly based on a type of relationship between objects, and cannot effectively describe multiple relationships in the real world. In this paper, we propose a multi-relationship bipartite network (MBN) model, which can describe multiple relationships between two types of objects, and realizes simple weighted bipartite network reconstruction. Our model contains three major modules, namely multi-relationship bipartite network modeling (MBNM), multi-relationship aggregation module (MAM) and network reconstruction module (NRM). In MBNM, a multi-relationship bipartite network is proposed to describe multiple relationships between two types of objects. In the MAM, considering that different relationships have different information for the model, we introduce a novel relationship-level attention mechanism, and the aggregation of multiple relationships is carried out through the importance of each relationship. Based on the learning framework, the NRM can learn the potential representations of nodes after multi-relationship aggregation, and design a nonlinear fusion mechanism to reconstruct weighted bipartite network. We conducted extensive experiments on three real-world datasets and the results show that multi-relationship aggregation can effectively improve the performance of the model. In addition, experiments also show that our model can outperform existing competitive baseline method.

Boolean networks have been widely used to model gene networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. Alternatively, bipartite Boolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such bipartite models, multiple TFs may simultaneously contribute to gene regulation by forming heteromeric complexes, thus giving rise to composition structures. Since bipartite Boolean models are relatively recent, an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes. Moreover, we present an additional mechanism where composition structures may arise as a result of multiple TFs binding to cis-regulatory regions and provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that though composition structures can severely restrict the number of Boolean functions (BFs) driving a gene, the two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are comparatively more restrictive. Finally, we find that composition structures are highly enriched in real networks, but this enrichment most likely comes from NCFs and RoFs.

The end of the line: has rapid transit contributed to the spatial diffusion of HIV in one of Canada’s largest metropolitan areas?

This paper focuses on the synchronization for the delayed bipartite networks with non-delay and delay couplings. Different from single networks, the nodes of bipartite networks can be divided into two disjoint nonempty sets according to their properties or dynamics characteristics, and the nodes in different sets can be connected by edges while the nodes in the same set cannot be connected. In this bipartite network model, the node and coupling delays are simultaneously considered, and both the non-delay and delay coupling matrices are undirected and weighed. Furthermore, the intermittent control method used in this paper can be aperiodic while in most previous works are periodic. Based on the Lyapunov function method, block multiplication of matrices and other mathematical techniques, several synchronization protocols are rigorously induced. Finally, the proposed control method is evaluated by utilizing numerical simulation.

SummaryIntrusion detection systems (IDSs) are the major component of safe network. Due to the high volume of network data, the false alarm report of intrusion to the network and intrusion detection accuracy is the problem of these security systems. The reliability of Internet of Things (IoT) connected devices based on security model is employed to protect user data and preventing devices from engaging in malicious activity. In this article, intrusion detection framework using auto‐metric graph neural network optimized with hybrid woodpecker mating and capuchin search optimization algorithm in IoT Network (IDF‐AGNN‐HYB‐WMA‐CSOA‐ IoT) is proposed. Initially the attacks affected in the IoT data is taken from the dataset such as CSIC 2010 dataset, ISCXIDS2012 dataset, then these data are preprocessed and the features are extracted to remove the redundant information using improved random forest with local least squares. Then the malicious attacks and the normal attacks are classified using the auto‐metric graph neural network. At last hybrid woodpecker mating and capuchin search optimization algorithm (Hyb‐WMA‐CSOA) is utilized to optimize the weight parameters of AGNN. The performance of ISCXIDS2012 dataset of the proposed method shows higher accuracy 25.37%, 29.57%, and 18.67%, compared with existing methods, such as IDF‐ANN‐IoT, IDF‐BMM‐IoT and IDF‐DNN‐IoT respectively.

Exploring how connection strengths between nodes affect the cost in controlling complex networks with fixed topological structure is an important issue both in theory and applications. In this paper, by considering optimal control of the networks, a matrix function optimization model is proposed to address such an issue. With pre-given input matrix, a normalized gradient descent method (NPGM) is developed to solve the optimization problem, so as to obtain the optimal connection strength matrix. With proposed NPGM, we find that a network adaptively changes its connection strength such that several control-flow subnetworks are self-formed. Moreover, we further point out that the control cost with optimal weight matrix is smallest when pre-located controller sources distribute evenly. These findings provide a comprehensive understanding of the impact of connection link weight on control cost for complex networks.

This paper aims at targeting some relevant issues for clustering tennis players and tournaments: (i) it considers players, tournaments and the relation between them; (ii) the relation is taken into account in the fuzzy clustering model based on the Partitioning Around Medoids (PAM) algorithm through spatial constraints; (iii) the attributes of the players and of the tournaments are of different nature, qualitative and quantitative. The proposal is novel for the methodology used, a spatial Fuzzy clustering model for players and for tournaments (based on related attributes), where the spatial penalty term in each clustering model depends on the relation between players and tournaments described in the adjacency matrix. The proposed model is compared with a bipartite players-tournament complex network model (the Degree-Corrected Stochastic Blockmodel) that considers only the relation between players and tournaments, described in the adjacency matrix, to obtain communities on each side of the bipartite network. An application on data taken from the ATP official website with regards to the draws of the tournaments, and from the sport statistics website Wheelo ratings for the performance data of players and tournaments, shows the performances of the proposed clustering model.

This research makes use of evidence that the distribution of crime by census tracts in large cities follows a Power Law. This means that there are few places that concentrate many crimes and many places that concentrate few crimes. In this article we investigate how modeling complex networks and genetic algorithms can help to understand the behavior of samples representing views of part of the map of crimes of a large metropolis. The representation of the network is a planar graph where the nodes are the centroids of census tracts, the edges represent the adjacency between the tracts, and each node has a weight representing the number of crimes recorded in the census tract. The problem of this research lies in the context of the study of sampling distributions that have long tails (e.g. the weight of the nodes of the graph follows a Power Law). In particular, we describe a genetic algorithm to explore the space of possible samples of the initial distribution (plotted crimes throughout the city) so that the maximum number of samples holds features to follow a Power Law with an exponent close to the original distribution.

In this paper, the novel study of the multilayered network model for the disrupted infrastructure of the 5G mobile network is introduced. The aim of this study is to present the new way of incorporating different types of networks, such as Wireless Sensor Networks (WSN), Mobile Ad-Hoc Networks (MANET), and DRONET Networks into one fully functional multilayered network. The proposed multilayered network model also presents the resilient way to deal with infrastructure disruption due to different reasons, such as disaster scenarios or malicious actions. In the near future, new network technologies of 5G networks and the phenomenon known as the Internet of Things (IoT) will empower the functionality of different types of networks and interconnects them into one complex network. The proposed concept is oriented on resilient, smart city applications such as public safety and health and it is able to provide critical communication when fixed network infrastructure is destroyed by deploying smart sensors and unmanned aerial vehicles. The provided simulations shows that the proposed multilayered network concept is able to perform better than traditional WSN network in term of delivery time, average number of hops and data rate speed, when disruption scenario occurs.

Ranking influential nodes in complex network using edge weight degree based shell decomposition

How the node’s vital and tie strength effect rumor spreading on social network

In the real industrial scenario, the setup times and delivery times are two non-negligible factors, but only few studies have considered the open shop scheduling problem with sequence-dependent setup times and delivery times (OSSP-STDT). In this paper, a mixed integer linear programming model is formulated firstly to accurately solve small-size problems, but it will fail when the size of the problem increases. Then, a complex scheduling network model is developed to characterize OSSP-STDT. After comprehensively considering the local topological features and time attributes in the complex network model, an effective heuristic rule based on complex network is established for solving large-size problems. Finally, an actual warehouse scheduling problem is converted into the aforementioned problem and used as one typical application scenario. Experiments have been conducted and computational results show that compared with exact solutions and meta-heuristics, the proposed algorithm can solve the large-size open shop scheduling problem with sequence-dependent setup times and delivery times more effectively and efficiently.

In this work, a novel process monitoring method in a block-wised partitioning manor is proposed for plant-wide processes which can be partitioned into several sub-blocks and monitored parallelly. The focus of this method is to reduce the high complexity of global plant-wide process, while to improve the efficiency of local feature extraction. In this method, considering that not all process knowledge is available in the block division process strategy, a novel community discovery (CD) algorithms, based on the similarity of neighbor node weighted Louvain, is introduced into the framework of the multi-block Bayesian inference and principal component analysis (PCA) based plant-wide process monitoring scheme. Firstly, the complex network (CN) theory is used to establish the network topology structure for the global variables of the plant-wide process. Secondly, by analyzing the graph characteristic structure, considering the connection strength between nodes, a more reasonable sub-block division is conducted according to the improved Louvain algorithm. Then, PCA method is used to establish process monitoring model for each sub-block to obtain sub-block monitoring statistics. Finally, the total joint statistics is obtained through Bayesian inference for fault detection. The feasibility and effectiveness, in terms of the detection performance, of this method are demonstrated in a simulated plant-wide process by compared with other state-of-the-art PCA based monitoring methods.

In the digital age, the networking field has undergone a marvelous transformation in various aspects. The fundamental networks struggle to build diverse, novel hardware-centric architectures to meet increasing demands such as network agility, flexibility, security, and efficiency. As the network applications become more dynamic, rapidly adaptable network infrastructure is essential to respond to the dynamiclarge-scale environments quickly. This ground breaking solution, Software Defined Networking (SDN), is increasing due to the decoupling nature of data and control planes. A Multi Controller Software Defined Network (MC-SDN) is a revolutionary concept comprising multiple controllers and switches separated using programmable features, enhancing network availability, management, scalability, and performance. The MC-SDN is a potential choice for managing large, heterogeneous, complex industrial networks. Despite the rich operational flexibility of MC-SDN, it is imperative to protect the network deployment with proper protection against potential vulnerabilities that lead to misuse and maliciou sactivities on the MC-SDNstructure. The security holes in the MC-SDN structure significantly impact network surviv ability and performance efficiency. Hence, detecting MC-SDN security attacks is crucial to improving network performance. Accordingly, introduces and illustrates the key developments in the MC-SDN defense mechanisms proposed using the latest developments in blockchain technology and machine learning. It highlights and identifies the potential studies in SDN and its security frameworks that significantly contribute to the current literature and substantially impact the relevant research.

Multiscale identification of urban functional polycentricity for planning implications: An integrated approach using geo-big transport data and complex network modeling