Information In Complex Networks Research Articles

Searching for Heavy-Tailed Probability Distributions for Modeling Real-World Complex Networks

Perhaps the most recent controversial topic in network science research is to determine whether real-world complex networks are scale-free or not. Recently, Broido and Clauset [A.D. Broido, A. Clauset, Nature Communication, 10, 1017 (2019)] asserted that the degree distributions of real-world networks are rarely power law under statistical tests. Such complex networks, including social, biological, information, temporal, and brain networks, are often heavy-tailed where the assumption on the scale-free nature of real-world heavy-tailed networks become insignificant as the complex system evolves over time. The failure of power law distribution in fitting the degree distribution data is mainly due to the presence of an identifiable non-linearity within the entire degree distribution in a log-log scale of a complex heavy-tailed network. In this study, we attempt to address this issue by proposing a new class of heavy-tailed probability distributions for modeling the entire degree distributions of complex networks. We introduce a new family of generalized Lomax models (GLM) to capture the non-linearity of these heavy-tailed networks. These newly introduced GLM-type distributions provide better fitting and greater flexibility to the entire node degree distribution of complex networks. Several statistical properties of the proposed model, such as extreme value and inferential statistical properties, are derived into this context. Interestingly, the GLM family belongs to the basin of attraction of Frechet distribution, a heavy-tailed extreme value distribution. Rigorous experimental analysis showcases the excellent performance of the proposed family of distributions while fitting the heavy-tailed real-world complex networks over fifty real-world datasets in comparison with benchmark probability models. Our results show that GLM-type distributions are not rare, able to model almost 90% of the tested networks accurately compared to benchmark probability models.

Maximizing spreading in complex networks with risk in node activation

It is widely acknowledged that the initial spreaders play an important role in the spread of information in complex networks. Thus, a variety of centrality-based methods have been proposed for identifying the most influential spreaders. However, most existing studies overlook the fact that, in real social networks, it is more costly and difficult to convince influential individuals to act as initial spreaders, resulting in a high risk to maximal spreading. In this paper, we address this problem on the basis of the assumption that the activation of large-degree nodes carries a higher risk than that of small-degree nodes. We aim to identify the initial spreaders that most effectively maximize the spreading when considering both the activation risk and the outbreak size of the initial spreaders. Analysis of random networks reveals that the degree of the optimal initial spreaders does not correspond to the largest node degree in the network, but is instead determined by the infection probability and difference in activation risk among nodes with different degrees. We propose a risk-aware metric to identify the most effective spreaders in real networks. Numerical simulations show that this risk-aware metric outperforms the existing benchmark centralities in terms of maximizing the spreading.

On eccentricity-based entropy measures for dendrimers

The eccentricity-based entropy inspired by Shannon's entropy approach is the information-theoretic quantity to figure out the structural information of complex networks. The investigation for advance biomedical utilization of dendrimers has improved the synthesis of radical based molecules. Categorically, attaining radical dendrimers has initiated their use in different fields such as anti-tumor agents and as magnetic resonance imaging. The use of radical dendrimers has increased the possibility of establishing new kinds of devices based on para-magnetic axioms of organic radicals. In this article, we discussed dendrimer based on cyclotriphosphazene (N3P3) which has balanced edge groups and these are examined by EPR temperature spectrum. Firstly, we computed eccentricity-based indices and then we computed eccentricity based entropies by developing an acquaintance between these indices and their entropies. Moreover we presented our computed result numerically and graphically which leads to good importance of our contribution.

Spreading the information in complex networks: Identifying a set of top-N influential nodes using network structure

The real world contains many complex networks, including research networks, social networks, biological networks, and transport networks. Real-world complex networks are unconstrained and can be characterized as undirected and unweighted. Understanding and controlling the process of information propagation in such networks is significant for decision-making activities and has many uses, such as disease control, market advertising, rumor control, and innovation propagation. Identifying the influencers in complex networks is an important activity, as influencers play a key role in spreading information to aid the decision-making process. In this study, we consider the problem of identifying a set of top-N influential nodes for spreading the information in undirected and unweighted networks using the network structure in the absence of domain-specific knowledge. In this study, we propose a novel method that computes the ranking scores of the nodes in the network and considers the influence of other nodes simultaneously when forming the set of top-N influential nodes. The proposed method is different from other methods of identification of influential nodes in the network, in that it takes into consideration the position of the nodes in the network while computing the ranking score, thereby preventing the clustering of important nodes, which hampers the information flow. Experiments are performed using several real-world complex networks to demonstrate the effectiveness of the proposed method.

A Survey on Recent Methods of Finding Influential Nodes in Complex Networks

Influential nodes refer to the ability of a node to spread information in complex networks. Identifying influential nodes is an important problem in complex networks which plays a key role in many applications such as rumor controlling, virus spreading, viral market advertising, research paper views, and citations. Basic measures like degree centrality, betweenness centrality, closeness centrality are identifying influential nodes but they are incapable of largescale networks due to time complexity issues. Chen et al. [1] proposed semi-local centrality, which is reducing computation complexity and finding influential nodes in the network. Recently Yang et al. 2020 [2] proposed a novel centrality measure based on degree and clustering coefficient for identifying the influential nodes. Sanjay et al. 2020 [3] gave voterank and neighborhood coreness-based algorithms for finding the influenced nodes in the network. Zhiwei et al. 2019 [4] considered the average shortest path to discover the influenced node in the network. These are the few recent local,global and mixed centralities. In this paper, we show a broad view of recent methods for finding influential nodes in complex networks. It also analyzes the new challenges and limitations for a better understanding of each method in detail. The experimental results based on these methods show better performance compared with existing basic centrality measures.

Information diffusion under public crisis in BA scale-free network based on SEIR model — Taking COVID-19 as an example

In the view of the fact that online information in complex networks has an increasingly powerful impact on real society, an improved network public opinion diffusion model under public crisis is established in this paper. The model updates SEIR infectious disease model by using Mean Field Theory based on BA scale-free network, which presents scale-free characteristics as well as enhances the model accuracy and applicability. It conducts a sensitivity analysis and takes “2019-nCoV Explosion” as a sample, crawling through related 180-day microblogs, forwards, comments and likes on Weibo. The results demonstrate that the propagation is greatly influenced by propagation probability, the network structure, the initial spreader and social effects, which contributes to the understanding of network public opinion diffusion and the control of the information dissemination direction.

Identifying influential nodes in complex networks: Effective distance gravity model

The identification of important nodes in complex networks is an area of exciting growth due to its applications across various disciplines like disease control, data mining and network system control. Many measures have been proposed to date, but they are either based on the locality of nodes or the global nature of the network. These measures typically use the traditional Euclidean Distance, which only focuses on local static geographic distance between nodes but ignores the dynamic interaction between nodes in real-world networks. Both the static and dynamic information should be considered for the purpose of identifying influential nodes. In order to address this problem, we have proposed an original and novel gravity model with effective distance for identifying influential nodes based on information fusion and multi-level processing. Our method is able to comprehensively consider the global and local information of complex networks, and also utilizes the effective distance to incorporate static and dynamic information. Moreover, the proposed method can help us mine for hidden topological structure of real-world networks for more accurate results. The susceptible infected model, Kendall correlation coefficient and eight existing identification methods are utilized to carry out simulations on twelve different real networks.

No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.

Topic Relevance of Public Health Emergencies Influence on Internet Public Opinion Resonance: Simulation Based on Langevin’s Equation

Public health emergencies occurred frequently, which usually result in the negative Internet public opinion events. In the complex network information ecological environment, multiple public opinion events may be aggregated to generate public opinion resonance due to the topic category, the mutual correlation of the subject involved, and the compound accumulation of specific emotions. In order to reveal the phenomenon and regulations of the public opinion resonance, we firstly analyze the influence factors of the Internet public opinion events in the public health emergencies. Then, based on Langevin’s equation, we propose the Internet public opinion stochastic resonance model considering the topic relevance. Furthermore, three exact public health emergencies in China are provided to reveal the regulations of evoked events “revival” caused by original events. We observe that the Langevin stochastic resonance model considering topic relevance can effectively reveal the resonance phenomenon of Internet public opinion caused by public health emergencies. For the original model without considering the topic relevance, the new model is more sensitive. Meanwhile, it is found that the degree of topic relevance between public health emergencies has a significant positive correlation with the intensity of Internet public opinion resonance.

Reinforcement learning approach for robustness analysis of complex networks with incomplete information

Abstract Network robustness against sequential attacks is significant for complex networks. However, it is generally assumed that complete information of complex networks is obtained and arbitrary nodes can be removed in previous researches. In this paper, a sequential attack in complex networks is modeled as a partial observable Markov decision process (POMDP). Then a reinforcement learning (RL) approach for POMDP is proposed to analyze dynamical robustness of complex networks under sequential attacks, when information of networks is incomplete. According to this approach, an agent can learn to take action by exploiting experiences. To solve the problem of large state space in complex networks, deep Q -network algorithm is used to identify most damaging sequential attacks, as deep neural networks can build up progressively abstract representations of state space of complex networks. The performances of proposed approach are analyzed on scale-free networks and small-world networks. According to the numerical simulations, it is found that the RL-based sequential attacks perform better when load distributions are more heterogeneous and local connections are more significant. Furthermore, it is shown that increasing the proportions of observed and attacked nodes improves the performance of RL-based sequential attacks. Finally, the results are verified on the IEEE 300-bus system and the simulation results highlight the damages caused by RL-based sequential attacks.

Introducing a novel bi-functional method for exploiting sentiment in complex information networks

Introducing a novel bi-functional method for exploiting sentiment in complex information networks

Introducing a novel bi-functional method for exploiting sentiment in complex information networks

This paper elaborates on multilayer Information Network (IN) modelling, utilising graph mining and machine learning. Although, Social Media (SM) INs may be modelled as homogeneous networks, real-world networks contain multi-typed entities, characterised by complex relations and interactions posing as heterogeneous INs. For mining data whilst retaining semantic context in such complex structures, we need better ways for handling multi-typed and interconnected data. This work conceives and performs several simulations on SM data. The first simulation models information, based on a bi-partite network schema. The second simulation utilises a star network schema, along with a graph database offering querying for graph metrics. The third simulation handles data from the previous simulations to generate a multilayer IN. The paper proposes a novel bi-functional method for sentiment extraction of user reviews/opinions across multiple SM platforms, considering the concepts of supervised/unsupervised learning and sentiment analysis.

Lift the Grit’s Veil: The Literature Review and Prospects of Grit

In the era of the Internet, telecommuting, virtual office and other work modes are increasingly popular. Telecommuting and other working modes give employees more freedom and flexibility in work, which reduces the direct monitoring from the outside. The complex network information will also bring interference and task interruption. At this time, how to maintain the focus on the task and self-motivation becomes particularly important. Grit refers to the perseverance and passion for long-term goals. As a variable of self-driven orientation and future perspective, it is a psychological resource to boost the realization of goals. Firstly this paper analyzes the concept, variable differentiation and measurement of grit. Secondly, the positive influence of grit on the realization of goals is discussed. Finally, this paper discusses the practical significance and future research prospects.

Degree-based entropy of molecular structure of hyaluronic acid–curcumin conjugates

The graph entropy is an important quantity of information theory. It measures the structural information of chemical graphs and complex networks. The graph entropy measures have specific chemical applications in discrete mathematics, biology and chemistry. The main contribution of this paper is to study properties of graph entropies and then goes on to discuss the structure of hyaluronic acid (HA)–curcumin conjugates. Also we compute entropies of this structure by using certain degree-based topological indices with the help of information function.

A multifunctional quantum teleportation network

Quantum teleportation is one of the most basic quantum protocols, which transfers an unknown quantum state from one location to another through local operation and classical communication by using shared quantum entanglement without physical transfer of the information carrier. And it has been widely used in various quantum information protocols such as entanglement swapping, quantum repeaters, quantum gate teleportation, quantum computation based on measurement, and quantum teleportation networks, which have important application value in quantum computation and quantum information. Quantum teleportation is a naturally bipartite process, in which an unknown quantum state can only be transmitted from one node to another. With the further development of quantum information research, it is necessary to transfer quantum states or quantum information among more and more nodes. Multipartite quantum protocols are expected to form fundamental components for larger-scale quantum communication and computation. A bipartite quantum teleportation should be extended to a multipartite protocol known as a quantum teleportation network. In this paper, a multifunctional quantum teleportation network is proposed theoretically. We first propose a special method of constructing four-partite quantum resources in continuous variables (CVs), and based on this, construct two different types of CV quantum teleportation networks. One type of network contains just one quantum teleportation process consisting of a sender, a receiver and two controllers. In this type of network, the unknown quantum state can be recovered at any other node according to the requirement after the measurement in the input node, which enriches the transfer direction and transfer mode of the unknown quantum state. And meanwhile, the two controllers can control the transfer of a quantum state from the sender to the receiver by restricting the sender and receiver’s access to their information, which makes the quantum teleportation network controllable. The other type of network has two quantum teleportation processes, each containing only a sender, a receiver and no controllers, which increases the number of quantum states that can be transmitted. Then we analyze the dependence of the fidelity of each quantum teleportation network on different physical parameters, and compare the characteristics, advantages and disadvantages among different types of quantum teleportation networks. The scheme for constructing a multifunctional quantum teleportation network in this paper shows some advantages, such as the greater number of quantum nodes, diversity of types, simple operation procedure. And all these advantages provide a broader application prospect for establishing larger and more complex quantum information networks in the future and quicken the pace of the application of quantum information.

Network Representation Learning Algorithm Combined with Node Text Information

In a network data analysis task, network representation learning play an important role. It can obtain a low-dimensional representative vector from high dimensional features of nodes which will promote the efficiency of downstream analysis task. Many algorithms mainly based on the structure information of the network but for a complex network, there is a lot kinds of feature information available for representation learning which has an important impact on network data analysis task. Therefore, we propose a network representation learning algorithm that combines node text information. Before our algorithm, we encode the information of the nodes including structure and text, and the strategy of fusion is adopted in advance which splice structure and text information. And then we feed the splicing results into the neural network which is a multi-layer feedforward neural network with a tower structure to learn the nonlinear structure information of complex networks. Finally, we design the structure loss function that preserve structure similarity and text loss function that preserve text information respectively. And we use the joint training to make two aspects information interact continuously. We conduct experiment to evaluate our algorithm. The results on two real word networks show our algorithm has better performance compared with traditional algorithms.

Finding Distributed Needles in Neural Haystacks.

The human cortex encodes information in complex networks that can be anatomically dispersed and variable in their microstructure across individuals. Using simulations with neural network models, we show that contemporary statistical methods for functional brain imaging-including univariate contrast, searchlight multivariate pattern classification, and whole-brain decoding with L1 or L2 regularization-each have critical and complementary blind spots under these conditions. We then introduce the sparse-overlapping-sets (SOS) LASSO-a whole-brain multivariate approach that exploits structured sparsity to find network-distributed information-and show in simulation that it captures the advantages of other approaches while avoiding their limitations. When applied to fMRI data to find neural responses that discriminate visually presented faces from other visual stimuli, each method yields a different result, but existing approaches all support the canonical view that face perception engages localized areas in posterior occipital and temporal regions. In contrast, SOS LASSO uncovers a network spanning all four lobes of the brain. The result cannot reflect spurious selection of out-of-system areas because decoding accuracy remains exceedingly high even when canonical face and place systems are removed from the dataset. When used to discriminate visual scenes from other stimuli, the same approach reveals a localized signal consistent with other methods-illustrating that SOS LASSO can detect both widely distributed and localized representational structure. Thus, structured sparsity can provide an unbiased method for testing claims of functional localization. For faces and possibly other domains, such decoding may reveal representations more widely distributed than previously suspected.SIGNIFICANCE STATEMENT Brain systems represent information as patterns of activation over neural populations connected in networks that can be widely distributed anatomically, variable across individuals, and intermingled with other networks. We show that four widespread statistical approaches to functional brain imaging have critical blind spots in this scenario and use simulations with neural network models to illustrate why. We then introduce a new approach designed specifically to find radically distributed representations in neural networks. In simulation and in fMRI data collected in the well studied domain of face perception, the new approach discovers extensive signal missed by the other methods-suggesting that prior functional imaging work may have significantly underestimated the degree to which neurocognitive representations are distributed and variable across individuals.

Deep attributed network representation learning of complex coupling and interaction

Networks that can describe complex systems in nature are increasingly coupled and interacted, and effective modeling on complex coupling and interaction information is an important research direction of artificial intelligence. Representation learning provides us with a paradigm to solve such issues, but the current network representation learning methods are difficult to capture the coupling and interaction information in complex networks. In this paper, we propose a novel deep attributed network representation learning model framework (RolEANE), which can effectively preserve the highly nonlinear coupling and interactive network topological structure and attribute information. We design two different structural role proximity enhancement strategies for the deep autoencoder in the model framework, so that it can efficiently capture network topological structure and attribute information. In addition, the neighbor-modified Skip-Gram model in our model framework can efficiently and seamlessly integrate network topological structure and attribute information, and the selection of an appropriate representation learning output strategy can significantly improve the final performance of the algorithm. The experiments on four real datasets show that our method consistently outperforms the state-of-the-art network representation learning methods. On the node classification task, the average performance is improved by 4.52%–10.28% than the optimal baseline method; on the link prediction task, the average performance is 4.63% higher than the optimal baseline method.

Complex network graph embedding method based on shortest path and MOEA/D for community detection

As one of the main applications of graph embedding, community detection has always been a hot issue in the field of complex network data mining. This paper presents a complex network graph embedding method based on the shortest path matrix and decomposition multi-objective evolutionary algorithm (SP-MOEA/D) for community detection, which can better reflect the network structure at the level of network community structure. Firstly, by calculating the shortest path matrix between nodes in the network, the node relationship matrix is obtained by adding the node similarity. Next, aiming at the problem of community detection in disconnected networks, a decomposition-based multi-objective optimization method is proposed to assign distances to unrelated nodes. Then, the network similarity matrix is calculated based on the relationship matrix of network nodes, and the low-dimensional vector representation of nodes is obtained by random surfing strategy and multi-dimensional scaling method. Finally, the community structure of the network can be detected based on the obtained node representation structure. Starting from the essence of network structure and the tightness between nodes, this method can reflect the relationship characteristics of network nodes more effectively, and then obtain the vector representation of nodes which can more accurately reflect the information of community structure in networks. The test results on 11 networks show that the node vector representation results obtained by this method can better reflect the community structure information in complex networks.

Some New Bounds of Weighted Graph Entropies with GA and Gaurava Indices Edge Weights

Motivated by the concept of Shannon’s entropy, the degree-dependent weighted graph entropy was defined which is now become a tool for measurement of structural information of complex graph networks. The aim of this paper is to study weighted graph entropy. We used GA and Gaurava indices as edge weights to define weighted graph entropy and establish some bounds for different families of graphs. Moreover, we compute the defined weighted entropies for molecular graphs of some dendrimer structures.