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Community structure is a common phenomenon observed in various social networks. In this work, a novel community detection algorithm is proposed by estimating the effect of community structure on the average path length of a network. The Erdos-Renyi graph model is used as a reference to compute the change in the average path length of a network due to community formation. By experimenting with artificial networks, it is found that community structure in a social network contributes towards the lengthening of the average shortest path length. A random graph is found to have a lesser average shortest path length than a social network with community structure. As the size of individual communities increases, there is a decrease in the difference of average shortest path lengths, compared with a random graph containing an equal number of nodes and edges. This relationship is used to predict the average community size and their numbers in a network. The findings mentioned above are applied to the proposed algorithm. The proposed community detection algorithm is an enhancement over Fire Spread community detection algorithm (Pattanayak, 2019), in which the value of R for the R-radius neighborhood subgraph is automatically calculated.

It is challenging to deploy check-in nodes optimally in a complex network so as to perform specific check-in like services, e.g., fuel supplements etc., but crucial in many real-world applications. In this article, we propose and study the new optimization problem of placing check-in nodes with the minimum cost, i.e., the problem of finding the minimum-cost check-in nodes (MCCN), which is of great interests for real-world application situations, but even more difficult. We motivate the new algorithms through three typical worse cases by the often-used greedy-type algorithms, i.e., One-to-Many, Many-to-One, and Duplicate-Overrides. With this respect, the proposed novel optimization algorithms utilize a novel metric of contribution density for selecting check-in nodes iteratively, which successfully avoid the occurrence of the two worse cases of One-to-Many and Many-to-One. We also introduce an extra backward extraction step in one of new algorithms, which overcomes the crucial worse case of ``duplicate overrides'' and largely improves the algorithmic performance in solving the introduced optimization problem of MCCN. Meanwhile, we extend the new contribution density metric to a more general class of functions and study their effectiveness to eliminate the two worse cases of One-to-Many and Many-to-One; also, a detailed analysis on complexity and performance of the proposed algorithms is presented to show their numerical efficiency and accuracy. Extensive experiments over two classical artificial networks, i.e., BA network and ER network, and ten real-world networks, under two typical cost setups, show the proposed algorithms significantly outperform the four state-of-the-art algorithms. We also demonstrate that our proposed algorithms are much reliable and robust with different experiment settings of deployment cost and network-type and scale.

The design of the water distribution system is inherently linked to the execution of calculations, which aim, among other things, to determine the flow rate through individual pipes and the selection of diameters at the appropriate speed. Each step in the calculations is followed by an evaluation of the results and, if necessary, a correction of the data and further calculations. It is up to the designer to analyse the accuracy of the calculation results and is time-consuming for large systems. In this article, a diagnostic method for the results of hydraulic calculations, based on Kohonen Network, which classifies nominal diameters [DN] on the basis of data, in the form of flows, has been proposed. After calculating the new variant of the water distribution system, the individual calculation sections are assigned to the neurons of the topological map of Kohonen Network drawn up for nominal diameters. By comparing the diameter used for the calculation, with the diameter obtained on the topological map, the accuracy of the chosen diameter can be assessed. The topological map, created as a result of labelling the neurons of the output layer of the Kohonen Network, graphically shows the position of the classified diameter, relative to those diameters with similar input values. The position of a given diameter, relative to other diameters, may suggest the need to change the diameter of the pipe.

The similarity graphs of most spectral clustering algorithms carry lots of wrong community information. In this paper, we propose a probability matrix and a novel improved spectral clustering algorithm based on the probability matrix for community detection. First, the Markov chain is used to calculate the transition probability between nodes, and the probability matrix is constructed by the transition probability. Then, the similarity graph is constructed with the mean probability matrix. Finally, community detection is achieved by optimizing the NCut objective function. The proposed algorithm is compared with SC, WT, FG, FluidC, and SCRW on artificial networks and real networks. Experimental results show that the proposed algorithm can detect communities more accurately and has better clustering performance.

This paper attempts to describe and address a specific puzzle related to compositionality in artificial networks such as Deep Neural Networks and machine learning in general. The puzzle identified here touches on a larger debate in Artificial Intelligence related to epistemic opacity but specifically focuses on computational applications of human level linguistic abilities or properties and a special difficulty with relation to these. Thus, the resulting issue is both general and unique. A partial solution is suggested.

Purpose The state-of-the-art methods designed for overlapping community detection are limited by their high execution time as in CPM or the need to provide some parameters like the number of communities in Bigclam and Nise_sph, which is a nontrivial information. Hence, there is a need to develop the accuracy that represents the primordial goal, where the actual state-of-the-art methods do not succeed to achieve high correspondence with the ground truth for many instances of networks. The paper aims to discuss this issue. Design/methodology/approach The authors offer a new method that explore the union of all maximum spanning trees (UMST) and models the strength of links between nodes. Also, each node in the UMST is linked with its most similar neighbor. From this model, the authors extract local community for each node, and then they combine the produced communities according to their number of shared nodes. Findings The experiments on eight real-world data sets and four sets of artificial networks show that the proposed method achieves obvious improvements over four state-of-the-art (BigClam, OSLOM, Demon, SE, DMST and ST) methods in terms of the F-score and ONMI for the networks with ground truth (Amazon, Youtube, LiveJournal and Orkut). Also, for the other networks, it provides communities with a good overlapping modularity. Originality/value In this paper, the authors investigate the UMST for the overlapping community detection.

In this paper, we introduce a new approach for detecting community structures in networks. The approach is subject to modifying one of the connectivity-based community quality functions based on considering the impact that each community’s most influential node has on the other vertices. Utilizing the proposed quality measure, we devise an algorithm that aims to detect high-quality communities of a given network based on two stages: finding a promising initial solution using greedy methods and then refining the solutions in a local search manner.The performance of our algorithm has been evaluated on some standard real-world networks as well as on some artificial networks. The experimental results of the algorithm are reported and compared with several state-of-the-art algorithms. The experiments show that our approach is competitive with the other well-known techniques in the literature and even outperforms them. This approach can be used as a new community detection method in network analysis.

The intensive use of antibiotics in livestock practice has a negative impact on human health and increases the antibiotic resistance. In this study feasible data interpretation algorithm along with efficient extraction protocol were combined for selective analysis of three antibiotics in milk samples. Trimethoprim, sulphamethoxazole and oxytetracycline are widely used antibiotics in veterinary pharmaceuticals. The studied antibiotics were efficiently extracted from milk samples with solidification of floating organic droplet in dispersive liquid-liquid microextraction. This extraction protocol was optimized not only to maximize extraction recoveries but also to approach the lower residue limits specified by European Union. Artificial neural networks succeeded in resolving spectral overlap between the studied drugs. The network architecture was optimized and validated for accurate and precise analysis. The proposed method outweighs the reported chromatographic methods for being simple and inexpensive and compared favorable to official methods.

SummaryNatural decisions involve two seemingly separable processes: inferring the relevant task (task-belief) and performing the believed-relevant task. The assumed separability has led to the traditional practice of studying task-switching and perceptual decision-making individually. Here, we used a novel paradigm to manipulate and measure macaque monkeys’ task-belief, and demonstrated inextricable neuronal links between flexible task-belief and perceptual decision-making. We showed that in animals, but not artificial networks that performed as well or better than the animals, stronger task-belief is associated with better perception. Correspondingly, recordings from neuronal populations in cortical areas 7a and V1 revealed that stronger task-belief is associated with better discriminability of the believed-relevant but not the believed-irrelevant feature. Perception also impacts belief updating: noise fluctuations in V1 help explain how task-belief is updated. Our results demonstrate that complex tasks and multi-area recordings can reveal fundamentally new principles of how biology affects behavior in health and disease.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Fractals have been identified as a common feature in many natural and artificial networks that exhibit self-similarity of the topological patterns, i.e. different parts of the system have similar structures to each other as well as to the whole system. This study investigates the fractality in water distribution networks (WDNs) and the application of the fractal property in WDNs analysis. Specifically, we explore the existence of fractal topological patterns in eight real-world WDNs of different complexities by using the box-covering algorithm. The results demonstrate all of the studied WDNs are fractal. Moreover, the application of the fractal property is demonstrated via critical pipe identification and optimal rehabilitation of benchmark real-world WDNs. All results show that the fractal-based approach can achieve better or equally good solutions compared with conventional methods in a much more efficient way, e.g. via automation of some processes or significant reduction in the search space/components to consider.

The recent advances in the application of artificial neural networks in the biological field have been inspired by the functional organization of real biological structures (Draye et al.,1997a; Anastasio & Gad, 2007). The fascination exerted by the oculomotor system upon both engineers and neuroscientists have played an important role in this issue. In particular, since the definitive evidence of the existence of a neural integrator in the brainstem (Cheron et al., 1986a; Cannon & Robinson, 1987; Robinson, 1989 for a review) performing mathematical integration of the eye velocity into eye position signals, numerous artificial networks have been developed allowing a better understanding of the fundamental question of how the brain control movement. Such bio-mimetic strategy has recently permitted to elaborate different dynamic recurrent neural networks (DRNN) specifically dedicated to the command of humanoid robot (Tani et al., 2008). Hierarchical neuralinspired modules have also been proposed forming cascades of forward dynamics models (Jordan & Rumelhart, 1992; Kawato et al., 1987; Tani, 2003) in which top-down and bottomup influences allowed generating behavioural primitives. This Chapter describes the main steps performed in the development of our DRNN from the neural integrator models to those applied in the field of human movement control.

We discuss the problem of locating the rumored origin in the social networks. Earlier works focused on network structure and sensor nodes; we concentrate on the timestamp of infected nodes. We recommend a two-stage algorithm to find the root node in a network using neighbor nodes' timestamp. In the first stage, discover the neighbor nodes with the minimal timestamp. Second, find the origin node using the minimal timestamp of the friend nodes. Simulations illustrate that our proposed two-stage algorithm works well and determines the root node within few hops. We evaluate our algorithm on small- scale networks, large-scale networks, and artificial networks. Our numerical results also show that our recommended two-stage algorithm operates well to discover the root node accurately, with a higher success rate and fewer distance error hops.

SummaryThe convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic of olfactory circuits would evolve in artificial neural networks trained to perform olfactory tasks. Artificial networks trained to classify odor identity recapitulate the connectivity inherent in the olfactory system. Input units are driven by a single receptor type, and units driven by the same receptor converge to form a glomerulus. Glomeruli exhibit sparse, unstructured connectivity to a larger, expansion layer of Kenyon cells. When trained to both classify odor identity and to impart innate valence onto odors, the network develops independent pathways for identity and valence classification. Thus, the defining features of fly and mouse olfactory systems also evolved in artificial neural networks trained to perform olfactory tasks. This implies that convergent evolution reflects an underlying logic rather than shared developmental principles.

Exploring and understanding the temporal structure of dynamic networks attract extensive attention over the past few years. Most of these current research focuses on temporal community detection, evolution analysis or link prediction from a mission-oriented perspective. In fact, these three tasks should be not isolated but mutually reinforcing. Transforming these three tasks into a unified framework, it is crucial to extract the evolution pattern, which helps to understand the time-varying characteristics of temporal structure in essence. In addition, to the best of our knowledge, there is no work focusing on modeling and uncovering the local and global evolution pattern hidden in temporal community structure, simultaneously. In this paper, we propose a novel framework based on Orthogonal Nonnegative Matrix Factorization to Explore the Evolution Pattern (ONMF-EEP) for analyzing and predicting the time-varying structures in dynamic networks from local and global perspectives. The nature of this framework assumes that community structures are subject to a local evolution pattern (LEP) at each snapshot, and these LEPs are from a common global evolution pattern (GEP). The framework can synchronously detect temporal community structure, extract evolution pattern, and predict structure including communities and future snapshot links. The extensive experiments on real-world networks and artificial networks demonstrate that our proposed framework is highly effective on the tasks of dynamic network analysis.

As the community detection is able to facilitate the discovery of hidden information in complex networks, it has been drawn a lot of attention recently. However, due to the growth in computational power and data storage, the scale of these complex networks has grown dramatically. In order to detect communities by utilizing global approaches, it is required to have all the global information of the whole network; something which is impossible, because of the rapid growth in the size of the networks. In this paper, a local approach has been proposed based on the detection and expansion of core nodes. First, a community’s central node (core node) which has a high level of embeddedness is detected based on the similarity between graph’s nodes. By using this, the total weights of a weighted graph’s edges created. Following by that, the expansion of these nodes will be considered, by utilizing the concept of node’s membership based on the definition of strong community for weighted graphs. It can be seen that in detecting communities, the more accurate the weights of edges detected based on the node similarity, the more precise the local algorithm will be. In fact, the algorithm has the ability to detect all the graph’s communities in a network using local information as well as identifying various roles of nodes, either being (core or outlier). Test results on both real-world and artificial networks prove that the quality of the communities which are detected by the proposed algorithm is better than the results which are achieved by other state-of-the-art algorithms in the complex networks.

Social networking services (SNSs) are constantly used by a large number of people with various motivations and intentions depending on their social relationships and purposes, and thus, resulting in diverse strategies of posting/consuming content on SNSs. Therefore, it is important to understand the differences of the individual strategies depending on their network locations and surroundings. For this purpose, by using a game-theoretical model of users called agents and proposing a co-evolutionary algorithm called multiple-world genetic algorithm to evolve diverse strategy for each user, we investigated the differences in individual strategies and compared the results in artificial networks and those of the Facebook ego network. From our experiments, we found that agents did not select the free rider strategy, which means that just reading the articles and comments posted by other users, in the Facebook network, although this strategy is usually cost-effective and usually appeared in the artificial networks. We also found that the agents who mainly comment on posted articles/comments and rarely post their own articles appear in the Facebook network but do not appear in the connecting nearest-neighbor networks, although we think that this kind of user actually exists in real-world SNSs. Our experimental simulation also revealed that the number of friends was a crucial factor to identify users’ strategies on SNSs through the analysis of the impact of the differences in the reward for a comment on various ego networks.

Anomaly detection aims at finding unexpected patterns in data. It has been used in several problems in computer networks, from the detection of port scans and distributed denial‐of‐service (DDoS) attacks to the monitoring of time series collected from Internet monitoring systems. Data‐driven approaches and machine learning have seen widespread application on anomaly detection too, and this trend has been accelerated by the recent developments on Artificial Intelligence (AI) research. This chapter summarizes ongoing recent progresses on anomaly detection research. In particular, we evaluate how developments on AI algorithms bring new possibilities for anomaly detection. We cover new representation learning techniques such as Generative Artificial Networks and Autoencoders, as well as techniques that can be used to improve models learned with machine learning algorithms, such as reinforcement learning. We survey both research works and tools implementing AI algorithms for anomaly detection. We found that the novel algorithms, while successful in other fields, have hardly been applied to networking problems. We conclude the chapter with a case study that illustrates a possible research direction.

We introduce and analyze a continuous time and state-space model of opinion cascades on networks of large numbers of agents that form opinions about two or more options. By leveraging our recent results on the emergence of agreement and disagreement states, we introduce novel tools to analyze and control agreement and disagreement opinion cascades. New notions of agreement and disagreement centrality, which depend only on network structure, are shown to be key to characterizing the nonlinear behavior of agreement and disagreement opinion formation and cascades. Our results are relevant for the analysis and control of opinion cascades in real-world networks, including biological, social, and artificial networks, and for the design of opinion-forming behaviors in robotic swarms. We illustrate an application of our model to a multi-robot task-allocation problem and discuss extensions and future directions opened by our modeling framework.

Traditional image recognition is mainly based on shallow neural networks, mainly including artificial networks, but these shallow neural networks have limited feature extraction capabilities. This paper can speed up the operation efficiency of the model, eliminate over-fitting, and improve the recognition accuracy by adding batch normalization layers to the neural network. The experiments are based on the MNIST data set, and have fully verified the recognition ability depth model proposed in this article.

Virus and information spreading dynamics widely exist in complex systems. However, systematic study still lacks for the interacting spreading dynamics between the two types of dynamics. This paper proposes a mathematical model on multiplex networks, which considers the heterogeneous susceptibility and infectivity in two subnetworks. By using a heterogeneous mean-field theory, we studied the dynamic process and outbreak threshold of the system. Through extensive numerical simulations on artificial networks, we find that the virus’s spreading dynamics can be suppressed by increasing the information spreading probability, decreasing the protection power, or decreasing the susceptibility and infectivity.

With the development in the areas such as high performance graphic processing units (GPU), sensor technologies and artificial networks the goal of self-driving vehicles has never been closer. With the promise of massive decrease in the road accidents, self-driving cars have been the center of attention for the past few years. In this paper we discuss the major components of a self-driving vehicles along with the methodologies required to produce a self-driving vehicle. With the added advent of the 3D printed body our aim is to rapid prototype a 3D printed Electric Vehicle with the goal to test and improve the current self-driving algorithms. The chassis of the car, with an initial body design was simulated and tested along with some basic parameters such as vehicle speed acceleration and traction force. The paper also presents the simulated results of an end-to-end approach to steering vehicle using convolutional neural networks, that would later be the basis of the self-driving algorithm.

A social circle usually has some cores. Inspired by this phenomenon, we consider that a community in a complex network is also formed around one or several core vertices, called core region. We define the core score of a vertex to reflect its ability to attract other vertices. Utilizing the core scores of vertices, we address how to find the core region of one community, and then we apply local expansion and optimization to detect communities based on these core regions. Besides detecting community structure, our method can detect some abnormal vertices that are the hubs of network. Experimental results based on artificial networks and real-world networks show that our method is more effective than some usual methods.

Introduction Early judgment of the depth of burns is very important for the accurate formulation of treatment plans. In medical imaging the application of Artificial Intelligence has the potential for serving as a very experienced assistant to improve early clinical diagnosis. Due to lack of large volume of a particular feature, there has been almost no progress in burn field. Methods 484 early wound images are collected on patients who discharged home after a burn injury in 48 h, from five different levels of hospitals in Hunan Province China. According to actual healing time, all images are manually annotated by five professional burn surgeons and divided into three sets which are shallow(0–10 days), moderate(11–20 days) and deep(more than 21 days or skin graft healing). These ROIs were further divided into 5637 patches sizes 224 × 224 pixels, of which 1733 shallow, 1804 moderate, and 2100 deep. We used transfer learning suing a Pre-trained ResNet50 model and the ratio of all images is 7:1.5:1.5 for training:validation:test. Results A novel artificial burn depth recognition model based on convolutional neural network was established and the diagnostic accuracy of the three types of burns is about 80%. Discussion The actual healing time can be used to deduce the depth of burn involvement. The artificial burn depth recognition model can accurately infer healing time and burn depth of the patient, which is expected to be used for auxiliary diagnosis improvement.

The dynamic range of stimulus processing in living organisms is much larger than a single neural network can explain. For a generic, tunable spiking network we derive that while the dynamic range is maximal at criticality, the interval of discriminable intensities is very similar for any network tuning due to coalescence. Compensating coalescence enables adaptation of discriminable intervals. Thus, we can tailor an ensemble of networks optimized to the distribution of stimulus intensities, e.g., extending the dynamic range arbitrarily. We discuss potential applications in machine learning., 6 pages plus supplemental material

Inspired by the physiology of neuronal systems in the brain, artificial neural networks have become an invaluable tool for machine learning applications. However, their biological realism and theoretical tractability are limited, resulting in poorly understood parameters. We have recently shown that biological neuronal firing rates in response to distributed inputs are largely independent of size, meaning that neurons are typically responsive to the proportion, not the absolute number, of their inputs that are active. Here we introduce such a normalisation, where the strength of a neuron’s afferents is divided by their number, to various sparsely-connected artificial networks. The learning performance is dramatically increased, providing an improvement over other widely-used normalisations in sparse networks. The resulting machine learning tools are universally applicable and biologically inspired, rendering them better understood and more stable in our tests.

Duplication and divergence are two basic evolutionary mechanisms of bio-molecular networks. Real-world bio-molecular networks and their statistical characteristics can be well mimicked by artificial algorithms based on the two mechanisms. Bio-molecular networks consist of network motifs, which act as building blocks of large-scale networks. A fundamental question is how network motifs are evolved from long time evolution and natural selection. By considering the effect of various duplication and divergence strategies, it is founded that the underlying duplication scheme of the real-world undirected bio-molecular networks would rather follow the anti-preference strategy than the random one. The anti-preference duplication mechanism and the dimerization can lead to the formation of various motifs and robustly conserve proper quantities of motifs in the artificial networks as that in the real-world ones. Furthermore, the anti-preference mechanism and edge deletion divergence can robustly preserve the sparsity of the networks. The investigations reveal the possible evolutionary mechanisms of network motifs and have potential implications in the design, synthesis, and reengineering of biological networks for biomedical purpose.

El objetivo del artículo es analizar el impacto de distintos tipos de estructuras y de diferentes propiedades estructurales de las redes sociales sobre la tendencia a confiar en el interior de ellas. Para llevar a cabo dicho objetivo, se ha realizado una serie de experimentos virtuales con redes artificiales a través de técnicas de simulación computarizadas. Los resultados ponen de manifiesto el profundo efecto de las propiedades estructurales (densidad de red, índice de globalización de vínculos e índice de Gini de la distribución de vínculos) y del tipo de estructura (regular, aleatoria y mundo pequeño) de las redes sociales sobre la tendencia a confiar entre los agentes de una red. Además, se evalúa la tesis de Coleman sobre el control y la vigilancia de los jóvenes para no abandonar los estudios a partir de los datos suministrados por los experimentos virtuales realizados con las redes artificiales anteriores. L'objectiu de l'article és analitzar l'impacte de diferents tipus d'estructures i de diferents propietats estructurals de les xarxes socials sobre la tendència a confiar a l'interior d'aquestes. Per dur a terme aquest objectiu, s'ha fet una sèrie d'experiments virtuals amb xarxes artificials a través de tècniques de simulació informatitzades. Els resultats posen de manifest el profund efecte de les propietats estructurals (densitat de xarxa, índex de globalització de vincles i índex de Gini de la distribució de vincles) i del tipus d'estructura (regular, aleatòria i món petit) de les xarxes socials sobre la tendència a confiar entre els agents d'una xarxa. A més, s'avalua la tesi de Coleman sobre el control i la vigilància dels joves per no abandonar els estudis a partir de les dades subministrades pels experiments virtuals realitzats amb les xarxes artificials anteriors. The aim of this article is to analyze the impact of different social network structures and structural properties on the tendency to trust inside networks. To this end, a series of virtual experiments with artificial networks have been carried out using computer simulations. The results underscore the profound effect of the structural properties (network density, link globalization index and Gini index of the distribution of links) and type of structure (regular, random and small world) of social networks on the tendency to trust among the agents of a network. In addition, Coleman's theory on the control and surveillance of young people is evaluated to study the data provided by the virtual experiments with the previous artificial networks.

Nowadays there is a boom in social network data streaming from various fields of interest related to finance, engineering, medicine, and general sciences. All these data are modeled as graphs for better analysis. Community detection is one such mechanism for the analysis of such massive data. Many community detection algorithms exist in literature. The existing algorithms are compared by using either real-world or artificial networks (modeled as graphs) but not both. This paper aims to make a comparative study of two popular existing community detection algorithms both on real-world and synthetic data and verify their performance. The approach in this paper makes good use of recent advances in graphical modeling of different social networks. We generated a random graph that represents most of the observed properties of a real-world dataset. The experimental results are tabulated and the computed metrics help in inferring the suitability or scalability of an algorithm for small or massive datasets.

Condition-specific modules in multiple networks must be determined to reveal the underlying molecular mechanisms of diseases. Current algorithms exhibit limitations such as low accuracy and high sensitivity to the number of networks because these algorithms discover condition-specific modules in multiple networks by separating specificity and modularity of modules. To overcome these limitations, we characterize condition-specific module as a group of genes whose connectivity is strong in the corresponding network and weak in other networks; this strategy can accurately depict the topological structure of condition-specific modules. We then transform the condition-specific module discovery problem into a clustering problem in multiple networks. We develop an efficient heuristic algorithm for the S pecific M odules in M ultiple N etworks ( SMMN ), which discovers the condition-specific modules by considering multiple networks. By using the artificial networks, we demonstrate that SMMN outperforms state-of-the-art methods. In breast cancer networks, stage-specific modules discovered by SMMN are more discriminative in predicting cancer stages than those obtained by other techniques. In pan-cancer networks, cancer-specific modules are more likely to associate with survival time of patients, which is critical for cancer therapy.

This study uses simulation over real and artificial networks to compare the eventual adoption outcomes of network interventions, operationalized as idealized contagion processes with different sets of seeds. While the performance depends on the details of both the network and behaviour adoption mechanisms, interventions with seeds that are central to the network are more effective than random selection in the majority of simulations, with faster or more complete adoption throughout the network. These results provide additional theoretical justification for utilizing relevant network information in the design of public health behavior interventions.

Measurement of the spreading capability of nodes has been one of the most attractive challenges in the field of social networks. Because of the huge number of nodes in a network, it has appealed to many researchers to find an accurate measure which can potentially detect the spreading capability and rankings of nodes. Most of the available methods determine the spreading capability of nodes based on their topological locations. In this paper, however, we have proposed a new measure based on the basic notions in information theory to detect the spreading capability of nodes in networks on the basis of their topological information. The simulation and experimental results of investigating real-world and artificial networks show that the proposed measure is more accurate and efficient than the similar ones.

Ranking influential nodes of networks is very meaningful for many applications, such as disease propagation inhibition and information dissemination control. Taking multiple attributes into consideration is a hopeful strategy. However, traditional multi-attribute ranking methods have some defects. Firstly, the computational complexity of these methods is usually higher than O(n), inapplicable to large scale social networks. Secondly, contributions of different attributes are viewed as equally important, leading to the limited improvement in performance. This paper proposes a multi-attribute ranking method based on node position and neighborhood, with low computational complexity O(n). The proposed method utilizes iteration information in the K-shell decomposition to further distinguish the node position and also fully considers the neighborhood’s effect upon the influence capability of a node. Furthermore, the entropy method is used to weight the node position and neighborhood attributes. Experiment results in terms of monotonicity, correctness and efficiency have demonstrated the good performance of the proposed method on both artificial networks and real world ones. It can efficiently and accurately provide a more reasonable ranking list than previous approaches.