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Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user's geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need to be addressed to develop more effective LBRSs. In this paper, we propose a novel POI recommendation system, called LACF-Rec3, which employs a hybrid approach of link analysis (HITS-3) and collaborative filtering (CF-3) based on three visiting behaviors: frequency, variety, and repetition. HITS-3 identifies distinctive POIs based on user- and POI-visit patterns, ranks them accordingly, and recommends them to cold-start users. For existing users, CF-3 utilizes collaborative filtering based on their previous check-in history and POI distinctive aspects. Our experimental results conducted on a Foursquare dataset demonstrate that LACF-Rec3 outperforms prior methods in terms of recommendation accuracy, ranking precision, and matching ratio. In addition, LACF-Rec3 effectively solves the challenges of data sparsity, the cold-start issue, and tedium problems for cold-start and existing users. These findings highlight the potential of LACF-Rec3 as a promising solution to the challenges encountered by LBRS. [ABSTRACT FROM AUTHOR]

The analysis of the hyperlink structure of the web has led to significant improvements in web information retrieval. This survey study evaluates and analyzes relevant research publications on link analysis in web information retrieval utilizing diverse methods. These factors include the research year, the aims of the research article, the algorithms utilized to complete their study, and the findings received after using the algorithms. The findings revealed that Page Rank, Weighted Page Rank, and Weighted Page Content Rank are extensively employed by academics to properly analyze hyperlinks in web information retrieval. Finally, this paper analyzes the previous studies. [ABSTRACT FROM AUTHOR]

One of the critical problems fishermen face in deep-sea fishing is the lack of low-cost communication mechanisms to the shore. The Offshore Communication Network (OCN) is a network of fishing vessels at sea whose goal is to provide wireless internet over the ocean. The impact of extreme weather conditions on wireless signals, the inability to deploy additional infrastructure, the movements induced by sea waves, the expanded mobility freedom at sea, and the misalignment of directional antenna links are all unique challenges that cause abrupt signal quality fluctuations in OCN. For this reason, it is necessary to integrate near real-time link quality assessment to improve the resilience of communication. This paper examines the characteristics of marine wireless links and the factors impacting communication using data collected through sea-trial experiments involving multiple fishing vessels. The paper proposes a Bayesian framework for forecasting signal strength by employing historical and real-time data. This hybrid learning integrates offline and online probabilistic learning methods to provide intelligence at edge devices. The evaluation of the learning scheme on real datasets and the comparison with baseline methods under different communication contexts show improved predictive accuracy in OCN links.

The present work investigates a new relative entropy-regularized algorithm for solving the optimal transport on a graph problem within the randomized shortest paths formalism. More precisely, a unit flow is injected into a set of input nodes and collected from a set of output nodes with specified marginals, while minimizing the expected transportation cost, together with a paths-based relative entropy regularization term, providing a randomized routing policy. The main advantage of this new formulation is the fact that it can easily accommodate edge flow capacity constraints which commonly occur in real-world problems. The resulting optimal routing policy, i.e., the probability distribution of following an edge in each node, is Markovian and is computed after constraining the input and output flows to the prescribed marginal probabilities. In addition, experimental comparisons with other recently developed techniques show that the distance measure between nodes derived from the introduced model provides competitive results on semi-supervised classification tasks. [ABSTRACT FROM AUTHOR]

This paper tries to examine the domain and link analysis, webpage ranking, web trafficking and engagement, audience demography and also WIFs through a webometric study of Top 10 space organizations in the world. Identify the domains of the website; analyze the number of webpages, inlinks, self-links and calculate the simple/external/self-link impact factor of the space organizations. A key method to webometric studies (WIFs) we trying to use the google search engine that allow to calculations to be made of the total number of web pages of the site And total number of external backlinks of the site. The required data were collected in December 2022 using the google search engine for retrieving the number of web pages, Inlinks and Selflinks to the website. After analysis, we observed that NASA, ISRO, UKSA, and KARI is the most popular organization among them. [ABSTRACT FROM AUTHOR]

Reporting guidelines are tools to help improve the transparency, completeness, and clarity of published articles in health research. Specifically, the CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) statements provide evidence-based guidance on what to include in randomised trial articles and protocols to guarantee the efficacy of interventions. These guidelines are subsequently described and discussed in journal articles and used to produce checklists. Determining the online impact (i.e., number and type of links received) of these articles can provide insights into the dissemination of reporting guidelines in broader environments (web-at-large) than simply that of the scientific publications that cite them. To address the technical limitations of link analysis, here the Debug-Validate-Access-Find (DVAF) method is designed and implemented to measure different facets of the guidelines' online impact. A total of 65 articles related to 38 reporting guidelines are taken as a baseline, providing 240,128 URL citations, which are then refined, analysed, and categorised using the DVAF method. A total of 15,582 links to journal articles related to the CONSORT and SPIRIT initiatives were identified. CONSORT 2010 and SPIRIT 2013 were the reporting guidelines that received most links (URL citations) from other online objects (5328 and 2190, respectively). Overall, the online impact obtained is scattered (URL citations are received by different article URL IDs, mainly from link-based DOIs), narrow (limited number of linking domain names, half of articles are linked from fewer than 29 domain names), concentrated (links come from just a few academic publishers, around 60% from publishers), non-reputed (84% of links come from dubious websites and fake domain names) and highly decayed (89% of linking domain names were not accessible at the time of the analysis). In light of these results, it is concluded that the online impact of these guidelines could be improved, and a set of recommendations are proposed to this end. [ABSTRACT FROM AUTHOR]

Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need to be addressed to develop more effective LBRSs. In this paper, we propose a novel POI recommendation system, called LACF-Rec3, which employs a hybrid approach of link analysis (HITS-3) and collaborative filtering (CF-3) based on three visiting behaviors: frequency, variety, and repetition. HITS-3 identifies distinctive POIs based on user- and POI-visit patterns, ranks them accordingly, and recommends them to cold-start users. For existing users, CF-3 utilizes collaborative filtering based on their previous check-in history and POI distinctive aspects. Our experimental results conducted on a Foursquare dataset demonstrate that LACF-Rec3 outperforms prior methods in terms of recommendation accuracy, ranking precision, and matching ratio. In addition, LACF-Rec3 effectively solves the challenges of data sparsity, the cold-start issue, and tedium problems for cold-start and existing users. These findings highlight the potential of LACF-Rec3 as a promising solution to the challenges encountered by LBRS.

Smart grid construction provides the basic conditions for grid connection of renewable energy power generation. However, the grid connection of large-scale intermittent renewable energy sources increases the complexity of operational control of power systems. With the increase in intermittent renewable energy grid connections, distribution system operators must optimize and integrate these new participants to ensure the flexibility and stability of smart grids. Dividing the smart grid into logical clusters helps to overcome the problems caused by intermittent renewable energy grid connections. In this study, we propose a 2-step modeling approach that includes both link and leverage analyses to detect the network partitioning and assess the stability of the smart grids. Our experimental results of the link analysis show that, despite the identical scores in modularity and Silhouette Coefficients (SC), the total computational time of the linear programming model for linkage (CD1) is 29.8% shorter than that of the quadratic programming model for linkage (CD2) on 7 networks with fewer than 200 nodes, whereas CD2 is 29.5% faster than CD1 on 19 larger networks with more than 200 nodes. The leverage results of benchmark networks indicate that the computational time of each instance with the proposed linear programming model for leverage (ID1) and quadratic programming model for leverage (ID2) was substantially reduced, and the Critical Node Problem (CNP) results of medium- and large-scale networks were better than those reported in the literature, which play a significant role in smart grid optimization.

The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. [ABSTRACT FROM AUTHOR]

The paper was to analysis the websites of NCTE recognized Physical Education courses, offered by Universities of India. The paper investigates such as the website page size (in bytes) and the Number of Web Pages, Total Links, Self Links, External Links and Back link or Incoming Links and the data were calculated as Simple Links Web Impact Factor, Self-Links Web Impact Factor, External Links Web Impact Factor and Revised Web Impact Factor, ranking To 85 websites of recognized Physical Education courses, offered by Universities of NCTE. Pearson correlation coefficient (r) was used to establish the association or closeness in ranking based on SWIF and R-WIF. Also this study SocSciBot 4.0 has been used for created link topology. The study revealed the four main aspect that the 1) Simple Web Impact Factor (Website Quality), Sai Nath University-Ranchi was the first position with 64.8750 SWIF 2) Revised Web Impact Factor (Website Popularity), Bharathiar University-Coimbatore was the first position with 671.9590 is RWIF 3) Pearson correlation coefficient (r) value is r = 0.02, which means 'Very Week Positive Correlation' among Universities 4) Link Topology, are not well connected among Universities of NCTE. [ABSTRACT FROM AUTHOR]

This study attempts to analyze patents as cited/mentioned documents to better understand the interest, dissemination and engagement of these documents in social environments, laying the foundations for social media studies of patents (social Patentometrics).Particularly, this study aims to determine how patents are disseminated on Twitter by analyzing three elements: tweets linking to patents, users linking to patents, and patents linked from Twitter. To do this, all the tweets containing at least one link to a full-text patent available on Google Patents were collected and analyzed, yielding a total of 126,815 tweets (and 129,001 links) to 86,417 patents. The results evidence an increase of the number of linking tweets over the years, presumably due to the creation of a standardized patent URL ID and the integration of Google Patents and Google Scholar, which took place in 2015. The engagement achieved by these tweets is limited (80.2% of tweets did not attract likes) but increasing notably since 2018. Two super-publisher twitter bot accounts (dailypatent and uspatentbot) are responsible of 53.3% of all the linking tweets, while most accounts are sporadic users linking to patent as part of a conversation. The patents most tweeted are, by far, from United States (87.5% of all links to Google Patents), mainly due to the effect of the two super-publishers. The impact of patents in terms of the number of tweets linking to them is unrelated to their year of publication, status or number of patent citations received, while controversial and media topics might be more determinant factors. However, further research is needed to better understand the topics discussed around patents on Twitter, the users involved, and the metrics attained. Given the increasing number of linking users and linked patents, this study finds Twitter as a relevant source to measure patent-level metrics, shedding light on the impact and interest of patents by the broad public. [ABSTRACT FROM AUTHOR]

RoleSim and SimRank are among the popular graph-theoretic similarity measures with many applications in, e.g., web search, collaborative filtering, and sociometry. While RoleSim addresses the automorphic (role) equivalence of pairwise similarity which SimRank lacks, it ignores the neighboring similarity information out of the automorphically equivalent set. Consequently, two pairs of nodes, which are not automorphically equivalent by nature, cannot be well distinguished by RoleSim if the averages of their neighboring similarities over the automorphically equivalent set are the same. To alleviate this problem: 1) We propose a novel similarity model, namely RoleSim*, which accurately evaluates pairwise role similarities in a more comprehensive manner. RoleSim* not only guarantees the automorphic equivalence that SimRank lacks, but also takes into account the neighboring similarity information outside the automorphically equivalent sets that are overlooked by RoleSim. 2) We prove the existence and uniqueness of the RoleSim* solution, and show its three axiomatic properties (i.e., symmetry, boundedness, and non-increasing monotonicity). 3) We provide a concise bound for iteratively computing RoleSim* formula, and estimate the number of iterations required to attain a desired accuracy. 4) We induce a distance metric based on RoleSim* similarity, and show that the RoleSim* metric fulfills the triangular inequality, which implies the sum-transitivity of its similarity scores. 5) We present a threshold-based RoleSim* model that reduces the computational time further with provable accuracy guarantee. 6) We propose a single-source RoleSim* model, which scales well for sizable graphs. 7) We also devise methods to scale RoleSim* based search by incorporating its triangular inequality property with partitioning techniques. Our experimental results on real datasets demonstrate that RoleSim* achieves higher accuracy than its competitors while scaling well on sizable graphs with billions of edges. [ABSTRACT FROM AUTHOR]

The present study focuses on the Social and Web presence of 27 Cultural Heritage Organisations in India. The purpose of this study is to investigate the domain authority, number of webpages, links, calculate the web impact factors and link-mentions on Social Networking Sites of the websites of Cultural Heritage Organisations and rank them accordingly. Analysing the websites of the Cultural Heritage Organisations it was found that majority of them have '.gov.in' domain name in their URLs. The study found that the highest domain authority (63) and page authority (51) was recorded by website of the Archaeological Survey of India. Indira Gandhi National Centre for the Arts ranked first in the global popularity ranking, with an Alexa rank of 289,037. Sahitya Akademi was first, with a bounce rate of 29.70 percent and the most average pages viewed by users per day (4.3). Lalit Kala Akademi ranked first in terms of estimated daily time spent on the site by visitors (06:14). With the highest number of in-links (646), Archaeological Survey of India ranked first. In WIF calculation, Archaeological Survey of India occupied first place with 7576.099 Simple Web Impact Factor. National Council of Science Museums with (0.865) Self-link Web Impact Factor holds the first position. External Web Impact Factor and Revised Link Web Impact Factor of North East Zone Cultural Centre with 1.137 EWIF and 0.549 RLWIF was found to be the highest. Sahitya Akademi (5,087) ranked first among all the Cultural Heritage Organisations under study, with the most linkmentions on Social Networking Sites (SNS). [ABSTRACT FROM AUTHOR]

The Greater China Region, commonly composed of China, Hong Kong, Macao, Singapore, and Taiwan, has seen the graying of their populations. With the global demographic shift that has been similarly observed in this region, senior gameplayers have increasingly represented an important market for digital game industries. Because of this global ageing/graying trend, the digital game industry has increasingly paid attention to the older digital gamers as a commercially profitable market segment. The sporadic data on senior gamers in the Greater China Region have supported the need to study this demographic segment to fill the literature gap to understand how media discourses represent this emerging demographic group. This text mining study examined their representations in the media discourses in this geographic region. Our analyses have found three extracted topics, "Game Genres," "Brain Training/Cognitive Benefits," and "Economic/Business Dimension," that best represent this increasingly influential demographic segment in the Greater China Region. Discussions and implications were provided. [ABSTRACT FROM AUTHOR]

This study examines the top 10 Indian Medical Institute websites ranked in NIRF (National Institutes Ranking Framework) 2020. The study focused on webometric analysis, which examines the domain, domain age, all three types of web impacts factors and Alexa traffic rank of websites etc. To collect data in this study, various small SEO tools such as smallsetools.com and dulichecker.com had used to find out domain age, page speed, domain authority, page authority and total, internal and external links of Medical Institute websites covered in this study. Findings revealed that the "All India Institute of Medical Sciences" institute website having the oldest domain registered on February 25, 1997, and achieved the highest Domain Authority score 58 with the first position among all institutes. It was found from the study that three institutes from Uttar Pradesh state were achieved palace in NIRF ranked top 10 medical universities of India. Among the top 10 medical institute websites, the highest page authority score was 57, achieved by "Banaras Hindu University." It was also observed from the findings that "Sanjay Gandhi Postgraduate Institute of Medical Science" has had the highest website speed in both mobile and desktop and first position in simple web impact factor and internal web impact factor. The external web impact factor "Post Graduate Institute of Medical Education and Research" got first among all universities. [ABSTRACT FROM AUTHOR]

Scientific software is a fundamental player in modern science, participating in all stages of scientific knowledge production. Software occasionally supports the development of trivial tasks, while at other instances it determines procedures, methods, protocols, results, or conclusions related with the scientific work. The growing relevance of scientific software as a research product with value of its own has triggered the development of quantitative science studies of scientific software. The main objective of this study is to illustrate a link-based webometric approach to characterize the online mentions to scientific software across different analytical frameworks. To do this, the bibliometric software VOSviewer is used as a case study. Considering VOSviewer's official website as a baseline, online mentions to this website were counted in three different analytical frameworks: academic literature via Google Scholar (988 mentioning publications), webpages via Majestic (1,330 mentioning websites), and tweets via Twitter (267 mentioning tweets). Google scholar mentions shows how VOSviewer is used as a research resource, whilst mentions in webpages and tweets show the interest on VOSviewer's website from an informational and a conversational point of view. Results evidence that URL mentions can be used to gather all sorts of online impacts related to non-traditional research objects, like software, thus expanding the analytical scientometric toolset by incorporating a novel digital dimension. [ABSTRACT FROM AUTHOR]

In online labor marketplaces two parties are involved; employers and workers. An employer posts a job in the marketplace to receive applications from interested workers. After evaluating the match to the job, the employer hires one (or more workers) to accomplish the job via an online contract. At the end of the contract, the employer can provide his worker with some rating that becomes visible in the worker online profile. This form of explicit feedback guides future hiring decisions, since it is indicative of worker true ability. In this paper, first we discuss some of the shortcomings of the existing reputation systems that are based on the end-of-contract ratings. Then we propose a new reputation mechanism that uses Bayesian updates to combine employer implicit feedback signals in a linkanalysis approach. The new system addresses the shortcomings of existing approaches, while yielding better signal for the worker quality towards hiring decision.

In online labor marketplaces two parties are involved; employers and workers. An employer posts a job in the marketplace to receive applications from interested workers. After evaluating the match to the job, the employer hires one (or more workers) to accomplish the job via an online contract. At the end of the contract, the employer can provide his worker with some rating that becomes visible in the worker online profile. This form of explicit feedback guides future hiring decisions, since it is indicative of worker true ability. In this paper, first we discuss some of the shortcomings of the existing reputation systems that are based on the end-of-contract ratings. Then we propose a new reputation mechanism that uses Bayesian updates to combine employer implicit feedback signals in a linkanalysis approach. The new system addresses the shortcomings of existing approaches, while yielding better signal for the worker quality towards hiring decision.

The Tor dark web network has been reported to provide a breeding ground for criminals and fraudsters who are exploiting the vulnerabilities in the network to carry out illicit and unethical activities. The network has unfortunately become a means to perpetuate crimes like illegal drugs and firearm trafficking, violence and terrorist activities among others. The government and law enforcement agencies are working relentlessly to control the misuse of Tor network. This is a study in the similar league, with an attempt to suggest a link-based ranking technique to rank and identify the influential hidden services in the Tor dark web. The proposed method considers the extent of connectivity to the surface web services and values of the centrality metrics of a hidden service in the web graph for ranking. The modified PageRank algorithm is used to obtain the overall rankings of the hidden services in the dataset. Several graph metrics were used to evaluate the effectiveness of the proposed technique with other commonly known ranking procedures in literature. The proposed ranking technique is shown to produce good results in identifying the influential domains in the tor network. [ABSTRACT FROM AUTHOR]

[Purpose/Significance]Library Portal, as the core of smart library construction, is the key window of providing online services. While how to construct a high-quality and high-effective library portal is the question that current smart library needs to solve urgently. [Methods/Proceeding] With the support of Link Analysis, this paper analyzes each three smart library portal models of two major Chinese third-party companies on Smart Library Portal Research and Development, through selecting the following seven website influencing factors: total number of the web pages, internal web link counts, external web link counts, External WIF, Baidu weight, Sogou weight and PR Value. Then the values of these seven influencing factors are synthetically sorted and sequenced based on Grey Relation Analysis. Thereafter, construction models of the two companies' smart library portal are analyzed and evaluated. [Consequence/Conclusion] The smart library portal models in the third-party companies have higher comprehensive quality and clear advantages. Then when smart library portal website is constructed, model to cooperate with the third-party company to develop solutions is encouraged. At the same time, to strengthen the input of human resources and the funding to construct a high-quality and most-effective library portal. [ABSTRACT FROM AUTHOR]

A framework to consensus opinion model within a networked social group is put forward. The current research in opinion formation within the groups is largely based on the opinion aggregation of each user of the network. However, the consistency of users in aggregation, social power, and the impact of each individual user of the group for opinion formation are not considered. In this paper, we investigate a consensus opinion model in social groups based on the impact of influential users and aggregation methods. In order to reach the consensus model, we aggregate the users' opinions. To maintain consistency, we propagate the opinion through the users to reach an agreement. This propagation will consider the influential users' impacts that have a crucial effect on its process. A novel method is proposed to detect the influential users and opinion propagation based on them to derive the opinion toward the networked social group. In particular, we applied optimism and pessimism scores as the users' personality to discover the influential users in the network. Considering that, we propagate the opinion based on two facts: 1) the impact of influential users, derived by the presence of an extremely confident individual in the network and 2) the impact of neighbors, induced by the presence of the users who have a connection with the current user. Then, we proposed the opinion aggregation of the group induced by the weighted averaging operator and fuzzy techniques. In order to evaluate the validity of the method, we used enormous data sets of Epinions and Etsy which are signed and unsigned, respectively.

Social tagging data, also known as folksonomy, are a valuable indication for the user's understanding of a resource. The nature of folksonomy data in which a user annotates a resource with their opinions provides immense potential to contribute to search personalization. The challenge lies in extracting interests from the folksonomy data and building accurate user profiles while maintaining their characteristics. Furthermore, the current state-of-the-art technologies that utilize folksonomy for search personalization have not fully exploited both multiple and temporal aspects in user profiles. In this paper, we propose a search personalization framework that constructs a user profile network with identification of the multiple topics of the user and the temporal values of tags. Then, the user profile network is further explored through a link analysis technique for the network to score the tags by their importance. The performance of the proposed framework is evaluated against various state-of-the-art folksonomy-based personalization models and it consistently outperforms all of the compared models under the conditions of the best combination of ranking functions and link analysis techniques.

This work elaborates on the important problem of (1) designing optimal randomized routing policies for reaching a target node t from a source note s on a weighted directed graph G and (2) defining distance measures between nodes interpolating between the least-cost (based on optimal movements) and the commute cost (based on a random walk on G), depending on a positive temperature parameter T. To this end, the randomized shortest path (RSP) formalism is rephrased in terms of Tsallis divergence regularization, instead of Kullback–Leibler divergence. The main consequence of this change is that the resulting routing policy (local transition probabilities) becomes sparser when T decreases, therefore inducing a sparse random walk on G converging to the least-cost directed acyclic graph when T → 0 . Experimental comparisons on node clustering and semi-supervised classification tasks show that the derived dissimilarity measures based on expected routing costs provide state-of-the-art results. The sparse RSP is therefore a promising model of movements on a graph, balancing sparse exploitation and exploration. [ABSTRACT FROM AUTHOR]

Structural node similarity is widely used in analyzing complex networks. As one of the structural node similarity metrics, role similarity has the good merit of indicating automorphism (isomorphism). Existing algorithms to compute role similarity (e.g., RoleSim and NED) suffer from severe performance bottlenecks and thus cannot handle large real-world graphs. In this paper, we propose a new framework, namely StructSim, to compute nodes' role similarity. Under this framework, we first prove that StructSim is an admissible role similarity metric based on the maximum matching. While the maximum matching is still too costly to scale, we then devise the BinCount matching that not only is efficient to compute but also guarantees the admissibility of StructSim. BinCount-based StructSim admits a precomputed index to query a single pair of node in O (k log D) time, where k is a small user-defined parameter and D is the maximum node degree. To build the index, we further devise an FM-sketch-based technique that can handle graphs with billions of edges. Extensive empirical studies show that StructSim performs much better than the existing works regarding both effectiveness and efficiency when applied to compute structural node similarities on the real-world graphs. [ABSTRACT FROM AUTHOR]

With the increasing complexity of scientific research and the expanding scale of projects, scientific research cooperation is an important trend in large-scale research. The analysis of co-authorship networks is a big data problem due to the expanding scale of the literature. Without sufficient data mining, research cooperation will be limited to a similar group, namely, a "small group", in the co-author networks. This "small group" limits the research results and openness. However, the researchers are not aware of the existence of other researchers due to insufficient big data support. Considering the importance of discovering communities and recommending potential collaborations from a large body of literature, we propose an enhanced clustering algorithm for detecting communities. It includes the selection of an initial central node and the redefinition of the distance and iteration of the central node. We also propose a method that is based on the hilltop algorithm, which is an algorithm that is used in search engines, for recommending co-authors via link analysis. The co-author candidate set is improved by screening and scoring. In screening, the expert set formation of the hilltop algorithm is added. The score is calculated from the durations and quantity of the collaborations. Via experiments, communities can be extracted, and co-authors can be recommended from the big data of the scientific research literature. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the domain authority, number of webpages, links, and calculate the web impact factor of six public libraries of India which are fully funded by Ministry of Culture with the supervision of administration. The data for the study were collected from websites of concerned libraries with the help of a suitable search engine, Open Site Explorer. The study found that the highest domain and page authority was recorded by Khuda Baksh Oriental Public Library and National Library, respectively. It also further revealed that excepting the two libraries, i.e., Khuda Baksh Oriental Public Library and Delhi Public Library, the internal equity-passing links and total internal links of rest of the libraries is zero. National Library leads with maximum total links and total equity-passing links, also with the highest followed linking root domains, total linking root domains, and linking C blocks, and concludes with the web impact factor of Central Secretariat Library recording the maximum, followed by National Library and Khuda Baksh Oriental Public Library.