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PurposeThis paper aims to explore the impact of supply chain centralities on sales performance moderated by reputation and stock listing.Design/methodology/approachEmpirical data on supply chain relationships were drawn from the context of electronics and electrical appliance industries in Tokyo, sourced from Teikoku Data Bank in year 2017–2018. On average, the authors analyzed an industrial network of 4,181 focal firms with 3.6 and 3.8 supplier and customer ties, respectively, using social networks and moderated regression analyses.FindingsThe authors find that in-(out-)degree, closeness and betweenness centralities are positively related to the focal firm’s annual sales performance. Hubs and authorities as ways of measuring reputation are found to not directly affect performance; hubs negatively moderate the relationship between betweenness and performance. Stock-listing was also found to negatively moderate the relationship between in-degree centrality and performance.Originality/valueThis study adds to existing literature by conducting a supply network analysis in a new industrial context, introducing a new method for assessing firm reputation in supply networks and showing how the structural characteristics of supply networks influence business performance.

Key nodes identification is an important way to analyze and understand the characteristics, structure, and functions of the complex network. In this paper, links in complex networks are taken as the basic unit, and the overlapping community in complex networks is obtained through the clustering analysis of links. Then, the importance of the node is judged according to the number of associations containing the node and the weight value of the association in the network, because the nodes Shared between communities have more influence on the functional structure of the network. Finally, the method is applied to rank the importance of nodes in IEEE standard 300 node system, and the results are verified by network conductivity. The comparison with the results of the Betweenness algorithm, HITS algorithm and Pagepank algorithm shows that the method presented in this paper can effectively identify the key nodes from the complex network.

Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users’ relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHAT model, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs.

The empirical focus is centered on the large social movement network created by the Koch Brothers to further their aims of transforming the US. The network was obtained by using the VOSON web crawler given a starting set of known allies of the Koch Brothers. This produced a large directed network with links between units. We propose using the idea of hubs and authorities as another way of considering the roles played by the units within this network. These roles may be more complex than has been realized. We have included an analysis of the core interests for the members of this network.

The ranking of network node importance is one of the most essential problems in the field of network science. Node ranking algorithms serve as an essential part in many application scenarios such as search engine, social networks, and recommendation systems. This paper presents a systematic review on three representative methods: node ranking based on centralities, PageRank algorithm, and HITS algorithm. Furthermore, we investigate the latest extensions and improvements of these representative methods, provided with several main application fields. Inspired by the survey of current literatures, we attempt to propose promising directions for future research. The conclusions of this paper are enlightening and beneficial to both the academic and industrial communities.

Purpose Therapeutic communities (TCs) use social learning between peers in treating substance abuse. One mechanism for fostering social learning is peer affirmations for prosocial behavior. The purpose of this study is to use consistency of affirmations as a test of whether social learning does occur. Design/methodology/approach Using the results of a social network survey of 50 women in a corrections-based TC, the authors compared affirmations and nonprogrammatic compliments exchanged between residents as two directed social networks. The authors evaluated consistency of judgment using the hubs and authorities algorithm, and tested to see whether more senior residents are more likely to be hubs, thereby showing more consistent judgment. Findings More senior residents show greater consistency with peers in program affirmations but not in nonprogrammatic compliments. Hub status in the network of affirmations increases most rapidly in the first 200 days of residence, with slower increases thereafter. Research limitations/implications This study is limited to one survey of the women in one TC. The external validity of the findings is therefore unclear. The results suggest that social learning of TC principles and prosocial behavior does occur and that it is not simply a function of popularity among peers. This seems to happen most rapidly in the first 200 days, suggesting that programs much shorter than six months may limit this process. Originality/value This is the first use of the hubs and authorities algorithm with a social network drawn from a therapeutic community and the first attempt to verify social learning through a social network analysis.

In this article, we introduce, test and discuss three quantum methods for computing hub and authority centrality scores in directed networks. The methods are based on unitary, continuous-time quantum walks; the construction of a suitable Hermitian Hamiltonian is achieved by performing a quantum walk on the associated bipartite graph. Two methods, called CQAu and CQAw, use the same evolution operator, inspired by the classical Hyperlink-Induced Topic Search (HITS) algorithm, but with different initial states; the computation of hub and authority scores is performed simultaneously. The third method, called CQG and inspired by classical PageRank, requires instead two separate runs with different evolution operators, one for hub and one for authority scores. The methods are tested on several directed graphs with different sizes and properties; a comparison with other well-established ranking algorithms is provided. CQAw emerges as the most reliable of the three methods and yields rankings that are largely compatible with results from HITS, although CQAu and CQG also present interesting features and potential for applications.

Billion digital images are uploaded every single day on the Internet and especially on social media. It is vital to develop effective and efficient methods that allow the retrieval of those images according to users' demands. Among the approaches that have been proposed for digital image retrieval is Automatic Image Annotation (AIA). AIA techniques automatically learn the visual representation of semantic concepts from a number of image samples, and use these concept models for tagging new images. Learning good concept models requires representative pairs of image-tags. Manual annotation is a hard and time-consuming task since a large number of images are necessary to create effective concept models. Moreover, human judgment may contain errors and subjectivity. Therefore, it is highly desirable to find ways for automatically creating training examples, i.e., pairs of images and tags. Contemporary social media, such as Instagram, contain images and associated hashtags, providing a source of indirect annotation. Instagram is a photo-oriented social media platform where users upload images and describe them with hashtags; thus, it might be a rich source for automatically creating pairs of image-tags for AIA. The thesis focuses on investigating Instagram images and hashtags as a field for AIA purposes. This primary research question is further analyzed through several studies: we define the portion of Instagram hashtags that are related to the visual content of images they accompany and we develop a methodology to locate stophashtags, i.e., common non-descriptive hashtags. We also employ the HITS algorithm in a crowdsourcing environment in order to filter Instagram hashtags and locate the ones that correspond to the visual content of Instagram images they accompany. Topic modelling of Instagram hashtags is introduced as a means for retrieving Instagram images in the traditional text-based information retrieval approach while transfer learning, utilizing filtered Instagram data (pairs of images and hashtags) is applied for a content-based image retrieval scenario. Completed

As the tsunami of data has emerged, search engines have become the most powerful tool for obtaining scattered information on the internet. The traditional search engines return the organized results by using ranking algorithm such as term frequency, link analysis (PageRank algorithm and HITS algorithm) etc. However, these algorithms must combine the keyword frequency to determine the relevance between user’s query and the data in the computer system or internet. Moreover, we expect the search engines could understand users’ searching by content meanings rather than literal strings. Semantic Web is an intelligent network and it could understand human’s language more semantically and make the communication easier between human and computers. But, the current technology for the semantic search is hard to apply. Because some meta data should be annotated to each web pages, then the search engine will have the ability to understand the users intend. However, annotate every web page is very time-consuming and leads to inefficiency. So, this study designed an ontology-based approach to improve the current traditional keyword-based search and emulate the effects of semantic search. And let the search engine can understand users more semantically when it gets the knowledge.

Instagram is a rich source for mining descriptive tags for images and multimedia in general. The tags–image pairs can be used to train automatic image annotation (AIA) systems in accordance with the learning by example paradigm. In previous studies, we had concluded that, on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stop-hashtags, that are used across totally different images just for gathering clicks and for searchability enhancement. In this paper, we present a novel methodology, based on the principles of collective intelligence that helps in locating those hashtags. In particular, we show that the application of a modified version of the well-known hyperlink-induced topic search (HITS) algorithm, in a crowdtagging context, provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. As a proof of concept, we used the crowdsourcing platform Figure-eight to allow collective intelligence to be gathered in the form of tag selection (crowdtagging) for Instagram hashtags. The crowdtagging data of Figure-eight are used to form bipartite graphs in which the first type of nodes corresponds to the annotators and the second type to the hashtags they selected. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowdtagging task and then to identify the right hashtags per image.

This paper investigates the centrality of cruise ports in the Asian cruise shipping market while proposing the hubs and authorities centrality (HACC) metric as a directional synthesis of the hubs centrality and authorities centrality to explore cyclical and directional features of centrality in the cruise shipping network. With the development of the cruise shipping industry, research has been actively conducted with a particular focus on identifying the characteristics of hub ports. This paper employs social network analysis to investigate the HACC which is originally developed for analyzing the cruise port centrality problem. Empirical study implies that Ho Chi Minh City, Hong Kong, Nagasaki, Penang, Phuket, Port Klang, Shanghai, and Singapore (in alphabetical order) reflects particulars of cruise hub ports. One of the exceptional results of this paper is Ho Chi Minh City, Penang, Phuket, and Port Klang have demonstrated high HACC (refers to hub ports) while limited degree and betweenness centrality. In contrast, Busan and Keelung are not classified as hub ports.

With the increasing popularity of intelligent devices, the mobile apps market has exploded. Due to a large number of candidate app services, it has become very difficult for users to choose the mobile apps that he/she wants to install. Therefore, it is crucial to improve users' experience and make personalized recommendations. In some cases, the traditional recommendation methods can be convenient, but they still have some shortcomings, resulting in inaccurate recommendations in general. To address this issue, this paper proposes a method for mobile app recommendations that are based on the Hyperlink-Induced Topic Search (HITS) algorithm combined with association rules. This method integrates the authority and hub scores into the candidate applications through the download and rating information, and it not only considers the importance of mobile apps in association rules but also takes the reliability factor of users into account. Experiments with the Huawei application market datasets show that the proposed method significantly improves the recommendation accuracies compared with the traditional methods.

Due to the massive amount of data and information available in web pages, users use search engines to find the desired information from the World Wide Web. Search engines encounter a very challenging task to retrieve relevant information efficiently. When a user inputs the search parameters and keywords into the search engine, there are many web pages that match the user’s query. To overcome this challenge, the search engine utilizes ranking algorithm to rank the results. One of the most popular ranking algorithms is the Page Rank algorithm which we consider in this study. One of the challenges related to Page Rank algorithm is that many web pages resulted from the algorithm may not meet the intended search query. So, studies are required to develop and enhance such sort of algorithms including the Page Rank algorithm. This study presents the traditional Page Rank algorithm which focuses on the number of links, Hyperlink-Induced Topic Search algorithms which emphasizes hubs and authorities, and Weighted Page Rank algorithm which weighs in-links and out-links to rank pages. Additionally, it reviews the latest studies that improve page rank algorithms based on user behavior and interest. This study proposes an algorithm called Extended User Preference Based Weighted Page Ranking Algorithm (EUPWPR), which enhances the latest studies of page rank algorithms. Moreover, this study considers more parameters to compute the page rank and the results showed that considering more parameters makes the page rank values more precise.

A sincronização em redes complexas é um fenômeno presente em vários sistemas complexos, dos neurológicos aos elétricos e dos mecânicos aos sociais. O arquétipo mais bem-sucedido na descrição da emergência desse comportamento coletivo em sistemas complexos é fornecido pelo modelo de Kuramoto. Transtornos neurológicos como a epilepsia podem ser abstraídos matematicamente como atividades síncronas em redes complexas. A epilepsia é um transtorno neural de intensas atividades síncronas neurais causadas pelo aumento do fluxo sanguíneo no córtex cerebral, ocasionando crises convulsivas seguidas por desmaios. As crises convulsivas podem ser evitadas por fármacos antiepilépticos que impedem a emergência de sincronia nas redes neurais. Contudo, cerca de um terço dos pacientes medicados voltam a ter crises convulsivas. Diante do exposto, justifica-se a importância de estudos de reconhecimento dos padrões síncronos associados ao transtorno. Este trabalho não tem a pretensão de ser a "última palavra" na modelagem de um transtorno neural tão complexo. Assim sendo, objetiva-se investigar quais são as áreas mais influentes do córtex cerebral e como elas influenciam a dinâmica da sincronização associada à epilepsia. Para tanto, modelou-se córtex cerebral de um felino como uma rede complexa e, de modo a estudar a sincronização, utilizou-se o modelo de Kuramoto como regente da dinâmica entre as áreas do córtex. Utilizou-se o algoritmo Hypertext Induced Topic Search (HITS), para classificação de páginas web, com o intuito de identificar os nós mais influentes da rede do córtex cerebral felino. Foram obtidas métricas relativas às características da rede neural felina e aos seus nós mais influentes. No que concerne à dinâmica e métricas de sincronia global, mesoscópica e a microscópica, foram obtidos resultados para um cenário utilizando a rede original e outros dois cenários, nos quais foi considerada uma perturbação, com intuito de simular a ação de um fármaco antiepiléptico, essa reduziu em 50% a intensidade das conexões em um grupo de nós aleatórios e do grupo de nós escolhidos pelo algoritmo HITS. Por fim, constatou-se que a perturbação aplicada defasou os níveis de sincronia global, microscópica e mesoscópica da rede. Synchronization in complex networks is a phenomenon present in several complex systems, ranging from neurological to electrical and mechanical to social. The most successful archetype in describing the emergence of this collective behavior in complex systems is provided by Kuramoto model. Neurological disorders such as epilepsy can be abstracted mathematically as synchronous activities in complex networks. Epilepsy is a neural disorder related to the intense synchronous neural activities due to the increase of blood flow in the cerebral cortex, causing seizures followed by fainting. Seizures can be prevented by antiepileptic drugs that fend off the emergence of synchrony in neural networks. However, about a third of medicated patients experience seizures again. Thus, the importance of studies on the recognition of synchronous patterns associated with the disorder is justified. This work does not claim to be an ultimate in the modeling of such a complex neural disorder. Therefore, the objective is to investigate which are the most influential areas of the cerebral cortex and how they influence the dynamics of synchronization associated with epilepsy. To this end, a feline's cerebral cortex was modeled as a complex network and, in order to study synchronization, Kuramoto model was used to govern of the dynamics between the areas of the cortex. The Hypertext Induced Topic Search (HITS) algorithm was used to classify pages web, in order to identify the most influential nodes in the feline cerebral cortex network. Metrics concerning the characteristics of the feline neural network and its most influential nodes, were obtained. Regarding the dynamics and measures of global, mesoscopic and microscopic synchrony, results were obtained for a scenario using the original network and two other scenarios, in which it was considered a disturbance, in order to simulate the action of an antiepileptic drug, the disturbance reduced the intensity of connections of a group containing random nodes and the group with nodes chosen by the HITS algorithm by 50%. Finally, it was found that the applied disturbance lagged the global, microscopic and mesoscopic levels of the network. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES

Spaceflight microgravity affects normal plant growth in several ways. The transcriptional dataset of the plant model organism Arabidopsis thaliana grown in the international space station is mined using graph-theoretic network analysis approaches to identify significant gene transcriptions in microgravity essential for the plant&rsquo, s survival and growth in altered environments. The photosynthesis process is critical for the survival of the plants in spaceflight under different environmentally stressful conditions such as lower levels of gravity, lesser oxygen availability, low atmospheric pressure, and the presence of cosmic radiation. Lasso regression method is used for gene regulatory network inferencing from gene expressions of four different ecotypes of Arabidopsis in spaceflight microgravity related to the photosynthetic process. The individual behavior of hub-genes and stress response genes in the photosynthetic process and their impact on the whole network is analyzed. Logistic regression on centrality measures computed from the networks, including average shortest path, betweenness centrality, closeness centrality, and eccentricity, and the HITS algorithm is used to rank genes and identify interactor or target genes from the networks. Through the hub and authority gene interactions, several biological processes associated with photosynthesis and carbon fixation genes are identified. The altered conditions in spaceflight have made all the ecotypes of Arabidopsis sensitive to dehydration-and-salt stress. The oxidative and heat-shock stress-response genes regulate the photosynthesis genes that are involved in the oxidation-reduction process in spaceflight microgravity, enabling the plant to adapt successfully to the spaceflight environment.

Those wishing to influence policy and practice often aim to identify key individuals with whom to connect. For example, researchers may wish to connect with brokers on the edge of the policy world who can speak for them in policy discussions, or they may wish to identify political or professional opinion leaders and influence them directly. Network analysis offers a robust analytical approach to identifying these influential individuals. One way to do this is to ask people to nominate key players. However, reputational approaches may be subject to recall bias, incomplete, or otherwise unreliable. This subjectivity and partiality is unlikely to be randomly distributed, however. Being able to accurately identify other powerful and influential actors may be a characteristic of powerful and influential actors. Previous work suggests that those with an accurate picture of the social network are both more likely to be influential, and better able to manipulate network structure. Hubs and Authorities offers a way to identify (a) those who are agreed to be powerful and influential and (b) those who are good at identifying these Authorities. Using Hubs and Authorities captures centrality in directed networks in an intuitively appealing manner. In this paper, I explore Hubs and Authorities measures in a policy network of 225 nodes. Findings indicate that most people who are good at identifying Authorities are not themselves considered important. However, there is a small overlap between the Hubs and Authorities, consisting of two-to-three mid-level managers. I explore possible interpretations of these results using qualitative interviews of the same network members, in which they discuss the nature of power, influence, and network structure. Finally, I discuss implications for evidence use and particularly network interventions aiming to increase evidence use.

This paper takes web page ranking as the theoretical basis of the research, uses PageRank algorithm and HITS algorithm as auxiliary research, and integrates their important content to analyze and research the algorithm optimization of improving web page ranking. This paper takes the classic algorithms PageRank algorithm and HITS algorithm as the research objects, and optimizes and improves the ranking of web pages respectively. Through the ranking of web pages in search engines, the PageRank algorithm and the HITS algorithm can be regarded as a kind of node ranking algorithm, so they can be used to construct selective web page ranking strategies; in addition, these two algorithms have fast calculation speed and are suitable for large networks. Features, suitable for large-scale web page sorting. This article introduces the PageRank algorithm and the HITS algorithm into the web page ranking strategy. On this basis, it deeply discusses the applicability and ranking efficiency of these two algorithms in the ranking of complex web pages, and provides better for the future practice of complex web page ranking. The experimental results show that this research has made breakthrough optimizations for the algorithm design of web page ranking selection in the current Internet era, improved the use of PageRank algorithm and HITS algorithm in web page ranking, and arranged the rationality of web page ranking in search engines in a more reasonable manner. The development of page ranking is of great significance.

The topic crawler needs to filter for Web prior to crawl Web page, and the crawl strategy of traditional topic crawler is to evaluate the content or evaluate the link relation, for the former topic crawler usually lack of overall importance, it is difficult to reflect the whole situation of the Web, and the theme crawler implemented by the latter will crawl to a large number of topic irrelevant pages in the later stage of crawling. In this paper, a new topic crawler algorithm is designed. The SVSM model is built to calculate the topic relevance of web pages, and HITS algorithm is used to calculate the comprehensive score of web pages. The topic crawler crawls the web page in the order of ranking from highest to lowest. In this paper, the new crawler is compared with two traditional crawlers under the same experimental environment, and the accuracy of the new topic crawler is 23% higher than that of the traditional topic crawler.

Effective infantry training requires accurate pointing direction knowledge of soldier-carried weapons to determine hit or miss scores. Infantry training solutions, which transition seamlessly to operational use, enables soldiers to train with what they use in combat, thereby increasing training efficacy and soldier lethality. To meet these dual needs, BAE Systems is developing the High-precision Infantry Training System (HITS). HITS provides a day/night, weapon-mounted ballistics point-of-impact detection system, to be used for live force-on-force training at Combat Training Centers (CTCs). HITS is hardware agnostic, but presently leverages BAE Systems’ fielded FWSI (Family-of-Weapons Sights-Individual) IR camera optic system to provide a ruggedized, low-SWaP, weapon-mounted platform for high-precision gun barrel pointing angle determination. The magnetometer and Inertial Measurement Unit (IMU) sensors, housed in the FWS-I gunsight, provide approximate pointing direction information while the FWS-I IR camera produces IR imagery with a field-of-view centered on the pointing direction of the gun. HITS uses innovative computer-vision processing to calculate the gun’s correct pointing angles. HITS performs geometric alignment of the FWS-I acquired IR image to a reference image of the view. The reference image is created by projecting a pre-generated reference model of the training site, consisting of geo-located 3-D LIDAR point-cloud data, onto the image plane. Using the gunsight location information, obtained from a GPS-RTK, the correct pointing angles are calculated from the image alignment. The system has an initial pointing accuracy goal of two milliradians. In this paper, we provide an overview of the HITS algorithm components including reference image generation, image registration, and pointing angle determination.

The recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculatingKnearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.

This paper focuses on firm’s R&D activities in autonomous vehicles. Using patent information with patent map and HITS algorithm, we demonstrate in this paper that R&D patterns of automobile manufacturers, parts suppliers and ICT companies. First, our patent map shows increasing R&D activities of autonomous vehicles along with development of artificial intelligence (AI) technology in recent years. Second, regarding to the levels of autonomous vehicles technology, HITS algorithm shows activities of autonomous vehicles development changes from Lv.0 through Lv.5. Third, since the year 2000, R&D activities of 3D241 in these three industries are lively. Fourth, using HITS algorithm, we find that most of the top thirty companies of automotive driving patent ranking are automotive manufacturers. Fifth, since in the late 1990s, R&D activities in automotive suppliers and ICT companies are lively. Finally, R&D activities in ICT companies once have declined, but since 2015, R&D activities in ICT companies are lively.

Link analysis is an important way to discover potential web communities. This paper analyzes the characteristics of the Web link structure, and studies the traditional maximum flow algorithm and the maximum flow algorithm based on HITS algorithm for edge capacity allocation. The traditional maximum flow problem, but there are still shortcomings. Based on the analysis of existing link similarity definitions, this paper proposes a new definition of link similarity and topic dissimilarity to better describe the relationship between linked pages, and to measure based on link similarity and topic dissimilarity. The similarity of the pages gives a more reasonable and efficient maximum flow-side capacity allocation scheme. The community discovery of 6 topics shows that the maximum flow algorithm proposed in this paper using the link similarity and topic dissimilarity model to allocate edge capacity can better solve the problems of existing algorithms and significantly improve the quality of the Web community. This paper proposes new methods and ideas for page similarity measurement, and provides new strategies for improving the Web community discovery algorithm based on link analysis.

Background Essential proteins are an important part of the cell and closely related to the life activities of the cell. Hitherto, Protein-Protein Interaction (PPI) networks have been adopted by many computational methods to predict essential proteins. Most of the current approaches focus mainly on the topological structure of PPI networks. However, those methods relying solely on the PPI network have low detection accuracy for essential proteins. Therefore, it is necessary to integrate the PPI network with other biological information to identify essential proteins. Results In this paper, we proposed a novel random walk method for identifying essential proteins, called HEPT. A three-dimensional tensor is constructed first by combining the PPI network of Saccharomyces cerevisiae with multiple biological data such as gene ontology annotations and protein domains. Then, based on the newly constructed tensor, we extended the Hyperlink-Induced Topic Search (HITS) algorithm from a two-dimensional to a three-dimensional tensor model that can be utilized to infer essential proteins. Different from existing state-of-the-art methods, the importance of proteins and the types of interactions will both contribute to the essential protein prediction. To evaluate the performance of our newly proposed HEPT method, proteins are ranked in the descending order based on their ranking scores computed by our method and other competitive methods. After that, a certain number of the ranked proteins are selected as candidates for essential proteins. According to the list of known essential proteins, the number of true essential proteins is used to judge the performance of each method. Experimental results show that our method can achieve better prediction performance in comparison with other nine state-of-the-art methods in identifying essential proteins. Conclusions Through analysis and experimental results, it is obvious that HEPT can be used to effectively improve the prediction accuracy of essential proteins by the use of HITS algorithm and the combination of network topology with gene ontology annotations and protein domains, which provides a new insight into multi-data source fusion.

The world of networks inherently being complex, is a primitive based on graphs. It uses adjacency matrix for abstracting the topology of underlying expanse. This structure of graphs, single-handedly decides the entries of the adjacency matrix, and thus, the overall topology of the system being described. In this research, we open new horizons at the cost of conventions of traditional adjacency formulation. We argue that fresh perspectives need to be plugged into as adjacency entries for modeling, which need not necessarily be binary in nature, rather a real number between one and zero. This adjacency entry between one and zero can be interpreted as a probability of successful transmission in a communication network for example. Also, we roll out the viable idea that number of connections together with probability of successful transmission for a link combined, rules the crux of ranking nodes as decided by the HITS algorithm. Broader perspectives have been discovered, deviating from traditional approaches of capturing networks and an innovative computational insight has been presented to illustrate the relationship between reliability of links, connectivity and leadership in the community. In particular, results presented here are in agreement with work done in the area of trust, reputation and leadership in the sense that leaders should be able to attain highest amount of activity.

In recent years, telecommunication fraud has become more rampant internationally with the development of modern technology and global communication. Because of rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with big data issues in real-world implementations. Although our previous work, FrauDetector , addressed this problem and achieved some promising results, it can be further enhanced because it focuses only on fraud detection accuracy, whereas the efficiency and scalability are not top priorities. Other known approaches for fraudulent call number detection suffer from long training times or cannot accurately detect fraudulent phone calls in real time. However, the learning process of FrauDetector is too time-consuming to support real-world application. Although we have attempted to accelerate the the learning process of FrauDetector by parallelization, the parallelized learning process, namely PFrauDetector , still cannot afford the computing cost. In this article, we propose a highly efficient incremental graph-mining-based fraudulent phone call detection approach, namely FrauDetector + , which can automatically label fraudulent phone numbers with a “fraud” tag a crucial prerequisite for distinguishing fraudulent phone call numbers from nonfraudulent ones. FrauDetector + initially generates smaller, more manageable subnetworks from original graph and performs a parallelized weighted HITS algorithm for a significant speed increase in the graph learning module. It adopts a novel aggregation approach to generate a trust (or experience) value for each phone number (or user) based on their respective local values. After the initial procedure, we can incrementally update the trust (or experience) value for each phone number (or user) while a new fraud phone number is identified. An efficient fraud-centric hash structure is constructed to support fast real-time detection of fraudulent phone numbers in the detection module. We conduct a comprehensive experimental study based on real datasets collected through an antifraud mobile application called Whoscall . The results demonstrate a significantly improved efficiency of our approach compared with FrauDetector as well as superior performance against other major classifier-based methods.

Vulnerability assessment is the key to the security and stability analysis of power system. During the operation of power grid, the fault risk of electrical equipment is not fixed because of the influence of system operating conditions, operating environment and equipment itself, so the vulnerability of power system will also be changeable. Based on the existing research results of complex network theory, a vulnerability assessment method considering operational reliability is proposed in this paper. Firstly, the basic idea of reliability weighted vulnerability assessment is introduced. Then HITS algorithm and PageRank algorithm will be used to analyze the basic vulnerability of power grid topology. Moreover, the vulnerability index of power grid considering reliability weighted is calculated. Finally, numerical analysis focusing on the difference between basic and improved assessment method is tested in IEEE RTS-79 system, and the preventive planning suggestions will be given based on the vulnerability assessment results.

In this work, we propose a method for ranking efficient decision-making units (DMUs) that uses measures of dominance derived from social network analysis in combination with data envelopment analysis (DEA). For this purpose, a directed and weighted graph is constructed, in which the nodes represent the system's DMUs and the edges represent the relationships between them. The objective is to identify and rank the most important nodes by taking into account the influence or dominance relations between the DMUs. The method uses a weighted HITS algorithm to identify the hubs and the authorities in the network by assigning to each DMU two numbers, the authority weight and the hub weight. Additionally, this method allows for the identification of DMUs whose exclusion from the DEA analysis does not modify the efficiency values obtained for the remaining DMUs.

This paper builds an index family, named bi-directional h-index, to measure node centrality in weighted directed networks. Bi-directional h-index takes the directed degree centrality as the initial value and iteratively uses more network information to update the node’s importance. We prove the convergence of the iterative process after finite iterations and introduce an asynchronous updating process that provides a decentralized, local method to calculate the bi-directional h-index in large-scale networks and dynamic networks. The theoretical analysis manifests that the bi-directional h-index is feasible and significant for establishing a greater conceptual framework that includes some existing index concepts, such as lobby index, node’s h-index, c-index and iterative c-index. An example using journal citation networks indicates that the bi-directional h-index is different from directed degree centrality, directed node strength, directed h-degree and the HITS algorithm in ranking node importance. It is irreplaceable and can reflect these measures of node’s importance.

Scientific collaboration plays an important role in the knowledge production and scientific development. Researchers have investigated numerous aspects of scientific collaboration by constructing scientific collaboration networks. And we can perform node centrality analysis on the scientific collaboration networks to identify important scholars. In these collaboration networks, two scientists are linked if they have coauthored at least one paper and the way of constructing these networks is based on the assumption that each author’s contribution to an article is the same. However, the authors’ contributions to an article are unequal in reality and we should pay attention to the impact of this unequal credit allocation on the understanding of scientific collaboration. In this paper, we regard the first author as the most important contributor to an article and build a directed scientific collaboration network. Then we identify important scholars by analyzing this directed network. For one thing, we investigate the difference between the undirected and directed scientific collaboration network in network properties and centrality analysis. For another, we apply different centrality indices: betweenness, PageRank, SIR and HITS to the directed scientific collaboration network. As a result, we find that each indicator has a different performance and the PageRank algorithm and SIR show highly positive correlation with in-degree. The HITS algorithm also shows better property which can hep us distinguish potential young scholars and identify important collaborators.

A particularly helpful search of a network such as the Internet or a citation network not only finds nodes that satisfy some criteria but also ranks those nodes for importance to create what amounts to a “reading list”. In the recent past, there has been a large interest across a number of research communities in the analysis of complex networks. The selected set of pages from the World Wide Web can be modeled as a directed graph, where nodes are designated as individual pages, and the links as a connection between them. As the number of webpages to be ranked is in the billions, the computation is time-consuming and can take several days or more. Algorithms like PageRank, HITS, SALSA and their modifications has a challenge to deal with the size of the processed data. The need for accelerated algorithms is clear. This article presents the characteristics of three best known ranking algorithms and the assumptions for new algorithm development with first test runs.

This study developed a framework for combining multi-regional input-output analysis and network indicators to assess the interregional CO2 flows in China. The interregional CO2 flows of eight regions were calculated and visualized based on a multiregional input-output (MRIO) model for China. The focus of the research was intermediate use. The results of the network indicators showed that refined petroleum, coke, nuclear fuel and chemical products (07), and basic metals and fabricated metal sectors (09) played key roles in the complex networks. and these sectors in most regions controlled a large share of CO2 transfer by functioning as key hubs and authorities. They along with commerce, transport, storage, and post (16) acted as agents that brokered the CO2 flows within and between regions. The roles of some other industrial sectors were also identified, e.g., construction (15) functioned as the largest authority. The results demonstrated the importance and effectiveness of network indicators for identifying the characteristics of CO2 emissions embedded in the domestic supply chain, and provided new information relevant to policy implementation.

This paper addresses the problem of topic distillation on the World Wide Web, namely, given a typical user query to find quality documents related to the query topic. Connectivity analysis has been shown to be useful in identifying high quality pages within a topic specific graph of hyperlinked documents. The essence of our approach is to augment a previous connectivity analysis based algorithm with content analysis. We identify three problems with the existing approach and devise algorithms to tackle them. The results of a user evaluation are reported that show an improvement of precision at 10 documents by at least 45% over pure connectivity analysis.

Extracting opinion targets (features) and opinion words is a main task in aspect-based level sentiment analysis. In this paper, we have proposed a hybrid approach for mining features and opinion words based on an upgraded “double propagation” algorithm by adding rules that explore the semantic relations between many parts of speech in sentences, some regular expression rules and ontologies. We employed HITS algorithm to prune features. In our experiments on multiple data sets, we showed that our approach gives perfectly acceptable results.