Link Analysis Algorithms Research Articles

A Repartitioning Temporary Customized Model for Interest-Point Recommendations with Hyperlink-Induced Topic Search

Objectives: To determine the significance of each object (node) in a graph, researchers often employ link analysis techniques, such as the Hyperlink- Induced Topic Search (HITS) algorithm. This will be performed for several reasons, including analyzing the confidentiality of social networks and optimizing search results based on the hierarchical nature of the Internet’s interconnections. Methods: This work proposes a new version of HITS called the Boundary grading HITS method (BG-HITS). We offer a technique for calculating edge weights that uses just the graph’s hub and authority parameters but considers the significance of each edge, its associated relationships and associations, and other relevant qualities such as whether or not they are ”organization”. Findings: Experiments on both simulated and realworld web-graph data demonstrate conclusively that our suggested method, when combined with edge weighting, may mitigate the effects of superfluous edges and nodes on the analysis, yielding more favourable and objective results than the previous HITS approach. Novelty: HITS is a method for doing link analysis that treats all edges the same in every calculation, much like nearly all other link analysis algorithms. The novelty of the proposed work is, the value of edges in practice varies from case to case and is influenced by the connections and associations between the two terminals. This has been resolved in the proposed approach. Keywords: Graph; HyperlinkInduced Topic Search (HITS); Link Analysis; Edge Weight Analysis; Recommendation Model; Points of Interest (POI)

SPACE: Finding Key-Speaker in Complex Multi-Person Scenes

Intelligent image processing based on deep learning is a recently emerging artificial Intelligence method which is critical in the next generation computing systems. Key-speaker detection using image processing aims to find the speaker who plays the most important role in complex multi-person scenes. However, existing speaker detection methods just judge whether there is somebody talking. To overcome this problem, this paper proposes a novel key-speaker detection approach named Speaker Pose-Attention deteCtion modEl (SPACE). This approach extracts the space information and analyze people's attention gathering to find the key-speaker. The method consists of two main parts. The first part is our proposed Importance Detection Model (IDM) using human pose estimation and an effective evaluation strategy to find candidates of important speaker. The second part consists of Pose-Attention Graph (PAG) and link analysis algorithm. The PAG represents people's attention graph. The link analysis algorithm analyzes the connectivity of this graph to find the key-speaker. Due to the lack of available key-speaker detection datasets, a self-collected dataset containing speech and meeting scenes has been built to estimate SPACE model. The experimental results show that the proposed method can obtain the best detection accuracy compared with other existing methods.

A Study of Link Analysis Using Components, Issues, Algorithms, Applications and Tools

Link Analysis is a data-analysis technique in Network theory used for evaluating relationships between nodes. The relationships between various types of nodes can be identified as people, property and transactions. This paper focuses on the study of Link Analysis components like Link generation, Explicit and Aggregate links and Infrared relationships. Also, this paper discusses Link Analysis Algorithm, like PageRank, Weighted PageRank and HITS. In addition to that, tools and applications used in Link Analysis research is also presented in this paper.

A Link Analysis Algorithm for Identification of Key Hidden Services

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.

An Improvement of Link Analysis Algorithm to Mine Pertinent Links: Weighted HITS Algorithm based on additive fusion of graphs by Query Similarity

An Improvement of Link Analysis Algorithm to Mine Pertinent Links: Weighted HITS Algorithm based on additive fusion of graphs by Query Similarity

A Novel Vector-Based Dynamic Path Planning Method in Urban Road Network

The optimal path planning is one of the hot spots in the research of intelligence transportation and geographic information systems. There are many productions and applications in path planning and navigation, however due to the complexity of urban road networks, the difficulty of the traffic prediction increases. The optimal path means not only the shortest distance in geography, but also the shortest time, the lowest cost, the maximum road capacity, etc. In fast-paced modern cities, people tend to reach the destination with the shortest time. The corresponding paths are considered as the optimal paths. However, due to the high data sensing speed of GPS devices, it is different to collect or describe real traffic flows. To address this problem, we propose an innovative path planning method in this paper. Specially, we first introduce a crossroad link analysis algorithm to calculate the real-time traffic conditions of crossroads (i.e. the $CrossRank$ values). Then, we adopt a $CrossRank$ value based $A$ - $Star$ for the path planning by considering the real-time traffic conditions. To avoid the high volume update of $CrossRank$ values, a $R$ - $Tree$ structure is proposed to dynamically update local $CrossRank$ values from the multi-level subareas. In the optimization process, to achieve desired navigation results, we establish the traffic congestion coefficient to reflect different traffic congestion conditions. To verify the effectiveness of the proposed method, we use the actual traffic data of Beijing. The experimental results show that our method is able to generate the appropriate path plan in the peak and low dynamic traffic conditions as compared to online applications.

Micro- and macro-level churn analysis of large-scale mobile games

As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. A critical challenge for these platforms and services is to understand the churn behavior in mobile games, which usually involves churn at micro level (between an app and a specific user) and macro level (between an app and all its users). Accurate micro-level churn prediction and macro-level churn ranking will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking. For micro-level churn prediction, in view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To address macro-level churn ranking, we propose to construct a relationship graph with estimated micro-level churn probabilities as edge weights and adapt link analysis algorithms on the graph. We devise a simple algorithm SimSum and adapt two more advanced algorithms PageRank and HITS. The performance of our solutions for the two-level churn analysis problems is evaluated on real-world data collected from the Samsung Game Launcher platform.

Emerging Countries and Trends in the World Trade Network: A Link Analysis Approach

The landscape in the world trade network has changed in last few decades. This paper analyses World Trade Network (WTN) from 1990 to 2016, using the trade data available at the International Monetary Fund (IMF) website and presents the evolution of key players in the network using link analysis properties. Link Analysis analyzes the link strength between nodes of a network to evaluate the properties of the network. The paper uses link analysis algorithm such as PageRank, hubs, and authority to evaluate the strength or importance of nodes in the World Trade Network. A higher PageRank represents higher import dependencies, higher authority scores of a country denotes its significance to import from other hub countries, and a higher hub score indicates a country’s significance to export their final product to other authority sectors. The findings show the emergence of Asian countries, especially China, as key players in the world.

Reducing Web Spam using Content and Link Analysis

Techniques of search engine manipulation are increasing rapidly making the importance of anti-web spam filters evident. In this paper, we fuse content analysis metrics and link analysis algorithms to retrieve relevant documents while blocking spam pages. We compare the efficiency of the algorithm with well-known link algorithms. This implementation aims to maintain a high recall/precision ratio while using two levels of filtering. The hybrid implementation outperforms the popular HITS.

Research on Improved Algorithm of PageRank Based on Vector Space

The traditional PageRank algorithm, which is based on the link analysis algorithm, considers the randomness of user access behavior, but the relevance of the query topic is poor. In order to improve the relevance of the page data search and acquisition, this paper proposes a PageRank algorithm based on the lucent vector space scoring model. The algorithm builds a vector space model based on the web content characteristics, calculates the similarity of the subject content, and combines the original PageRank algorithm, the new PR value is obtained after weighted fusion. Experiments show that the improved algorithm reduces the number of irrelevant pages, and the PR value can better reflect the relevance of the topic.

Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model

In this document, the algorithm behind Google PageRanking and their techniques have been put up. The basic algorithm used by Google, for PageRanking and other applications are Markov model or Markov Chain model and Hidden Markov model. These algorithms are used to search and rank websites in the Google search engine. PageRank is a way of measuring the importance of website pages. Markov chain model and Hidden Markov model is a mathematical system model. It describes transitions from one state to another in a state space. The Markov model is based on the probability the user will select the page and based on the number of incoming and outgoing links, ranks for the pages are determined. HMM also finds its application within Mapper/Reducer. These algorithms are a link analysis algorithm.

A Survey Paper of Structure Mining Technique using Clustering and Ranking Algorithm

A survey of various link analysis and clustering algorithms such as Page Rank, Hyperlink-Induced Topic Search, Weighted Page Rank based on Visit of Links K-Means, Fuzzy K-Means.Ranking algorithms illustrated, Weighted Page Rank is more efficient than Hyperlink-induced Topic Search Whereas clustering algorithms has described Fuzzy Soft, Rough K-Means is a mixture of Rough K-Means and fuzzy softest and provide efficient results than Rough K-Means approach and K-means. The literature survey shows how these algorithms are used for link-analysis and extracts the information, including contents and images from web pages efficiently.In new algorithm Weighted Page Content Rank user can get relevant and important pages easily as it employs web structure mining and web content mining. A webpage ranking analysis can be apply on the scenario where the searching and interaction with the numerous web data is required, so in order to provide effective result the technique can be used.

Information Retrieval and Graph Analysis Approaches for Book Recommendation.

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.

IJARCCE - Computer and Communication Engineering

Today World Wide Web (WWW) is growing into eternity and it has massive wealth of information. Ordinary people also use the web to retrieve the needed and related semantic information. So that, mining the web is very essential in order to retrieve the necessary information for any user. Web mining can be categorised into the following, namely, Web Content Mining (WCM), Web Structure Mining (WSM) and Web Usage Mining (WUM). These categories can be reviewed by this paper. The two link analysis algorithms namely PageRank and Hyperlink Induced Topic Search are also discussed in the Web Structure Mining.

Link analysis algorithms for static concept location: an empirical assessment

During software evolution, one of the most important comprehension activities is concept location in source code, as it identifies the places in the code where changes are to be made in response to a modification request. Change requests (such as, bug fixing or new feature requests) are usually formulated in natural language, while the source code also includes large amounts of text. In consequence, many of the existing concept location techniques are based on text search or text retrieval. Such approaches reformulate concept location as a document retrieval problem. We refine and improve such solutions by leveraging dependencies between source code elements. Dependency information is used by a link analysis algorithm to rank the document space and to improve concept location based on text retrieval. We implemented our solution to concept location using the PageRank algorithm, used in web document retrieval applications. The results of an empirical evaluation indicate that the new approach leads to better retrieval performance than baseline approaches that use text retrieval and clustering. In addition, we present the results of a controlled experiment and of a differentiated replication to assess whether the new technique supports users in identifying the places in the code where changes are to be made. The results of these experiments revealed that the users exploiting our technique were significantly better supported in the identification of the code to be changed in response to a bug fixing request compared to the users who did not use this technique.

Experimental study of Web Page Ranking Algorithms

Today's life is very much influenced by the internet. Everything can be accessed by making a search on the Internet. Web browsers and web search engines are the two major components of web searching. Web browsers are the software applications and web search engines are the software itself. To achieve the goal of web searching web indexer plays a very important role of indexing the web pages by making use of link analysis algorithms. In this paper we are going to compare the three most commonly used link analysis algorithms which are page rank, weighted page rank and the hub & authority algorithms.

Using site-level connections to estimate link confidence

Search engines are essential tools for web users today. They rely on a large number of features to compute the rank of search results for each given query. The estimated reputation of pages is among the effective features available for search engine designers, probably being adopted by most current commercial search engines. Page reputation is estimated by analyzing the linkage relationships between pages. This information is used by link analysis algorithms as a query-independent feature, to be taken into account when computing the rank of the results. Unfortunately, several types of links found on the web may damage the estimated page reputation and thus cause a negative effect on the quality of search results. This work studies alternatives to reduce the negative impact of such noisy links. More specifically, the authors propose and evaluate new methods that deal with noisy links, considering scenarios where the reputation of pages is computed using the PageRank algorithm. They show, through experiments with real web content, that their methods achieve significant improvements when compared to previous solutions proposed in the literature. © 2012 Wiley Periodicals, Inc.

Impact and degree of user sociability in social media

Users’ posts receive feedback from other users in the form of comments, trackback, or recommendation in social media. These interactions form a graph in which the vertices represent a set of users, while the edges represent a set of feedback. Thus, the problem of users’ rankings can be approached in terms of the link analysis of the social interactions between the users themselves within this graph. Link analysis algorithms, such as PageRank and HITS, have often been applied for users’ rankings, especially for users’ reputation, but no consideration has been given to how the user’s sociability can affect the user’s reputation. We propose two factors that affect the score of every user, the user’s reputation, and the user’s sociability, to address this problem. We present novel schemes that can effectively and separately estimate the reputation and sociability of the users. Furthermore, we present schemes to measure the degree of the user’s sociability in a social network. Our experiments show that: (1) our schemes can effectively separate the user’s pure reputation from the user’s sociability (2) the pure reputation is capable of producing superior user ranking results than can previous work (3) the degree of user sociability for each social network varies and reveals significant characteristics of the corresponding network.

Integrating information retrieval, execution and link analysis algorithms to improve feature location in software

Data fusion is the process of integrating multiple sources of information such that their combination yields better results than if the data sources are used individually. This paper applies the idea of data fusion to feature location, the process of identifying the source code that implements specific functionality in software. A data fusion model for feature location is presented which defines new feature location techniques based on combining information from textual, dynamic, and web mining or link analyses algorithms applied to software. A novel contribution of the proposed model is the use of advanced web mining algorithms to analyze execution information during feature location. The results of an extensive evaluation on three Java systems indicate that the new feature location techniques based on web mining improve the effectiveness of existing approaches by as much as 87%.

"Towards more readable web: measuring readability of web pages based on link structure" by Adam Jatowt, Kouichi Akamatsu, Nimit Pattanasri, and Katsumi Tanaka with Ching-man Au Yeung as coordinator

Although Web search engines have become information gateways to the Internet, search results often contain pages that are difficult to understand for non-expert users, especially when queries contain technical or rare terms. Readability indexes are well-known measures for estimating text comprehensibility. However, readability indexes are not sufficient for evaluating the comprehensibility of Web pages, as they are designed for general purpose texts. In this research, we investigate the relationship between links and readability of text extracted from Web pages for two datasets, namely English and Japanese pages. We then describe a link analysis algorithm for measuring comprehensibility of Web pages based on the TrustRank algorithm originally used for combating Web spam. Lastly, we report results of preliminary studies to measure the correlation between search rank and readability of Web search results.