PageRank, HITS and a Unified Framework for Link Analysis

Ding, Chris; He, Xiangming; Husbands, Parry; Zha, Hongyuan; Simon, Horst D.

doi:10.1137/1.9781611972733.24

Cited by 60 publications

(71 citation statements)

References 1 publication

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“…There are several algorithms of the link analysis; e.g., the PageRank algorithm and HITS (i.e., Hypertext Induced Topic Selection) algorithm are two popular algorithms. PageRank considers the hyperlink weight normalization and the balance distribution of random surfers as the citation score [7]. HITS makes the differentiation between hubs and authorities, and estimates them in a mutually reinforcing way.…”

Section: Link Analysis Algorithmmentioning

confidence: 99%

Recommendations of Personal Web Pages Based on User Navigational Patterns

Huang¹,

Jhang²

2014

IJMLC

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Abstract-In this paper, we propose a web recommendation system where user navigational patterns can be extracted from web logs. First, the recommendation system discovers user concepts from web logs step-by-step, and then extracts the navigation patterns among these concepts. These navigational patterns are then used to generate recommendation web pages by matching the navigation behavior of a user personal knowledge base. The pages in a recommendation list are ranked according to their hub scores which are computed based on page connectivity information. The experimental results show that the web pages recommended by our system are of better quality and acceptable for humans from various domains, based on human evaluators ranking as well as quality-value-based performance measures. Index Terms-Information retrieval, recommendation system, web mining, web search.

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Section: Link Analysis Algorithmmentioning

confidence: 99%

Recommendations of Personal Web Pages Based on User Navigational Patterns

Huang¹,

Jhang²

2014

IJMLC

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“…We note that Ding et al [7] attempt to find a framework to unify the PageRank and HITS models. The unification is to establish a connection between the two models such that it becomes possible to use a simple count of the number of inlinks to a webpage as an approximation of its PageRank.…”

Section: H Additional Related Workmentioning

confidence: 99%

Mining significant semantic locations from GPS data

2010

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With the increasing deployment and use of GPS-enabled devices, massive amounts of GPS data are becoming available. We propose a general framework for the mining of semantically meaningful, significant locations, e.g., shopping malls and restaurants, from such data.We present techniques capable of extracting semantic locations from GPS data. We capture the relationships between locations and between locations and users with a graph. Significance is then assigned to locations using random walks over the graph that propagates significance among the locations. In doing so, mutual reinforcement between location significance and user authority is exploited for determining significance, as are aspects such as the number of visits to a location, the durations of the visits, and the distances users travel to reach locations. Studies using up to 100 million GPS records from a confined spatio-temporal region demonstrate that the proposal is effective and is capable of outperforming baseline methods and an extension of an existing proposal.

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“…The latter has been extensively investigated and several link analysis algorithms have been developed for this [11,6,22,9,25]. Even some unifying frameworks for link analysis algorithms exist [12]. Interesting work on ranking elements in relational databases based on measures from Information Retrieval (IR) is described in [3], however the notion of "link" provided by the queries has not been considered there.…”

Section: Related Workmentioning

confidence: 99%

Relational link-based ranking

GEERTS¹,

MANNILA²,

TERZI³

2004

Proceedings 2004 VLDB Conference

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Link analysis methods show that the interconnections between web pages have lots of valuable information. The link analysis methods are, however, inherently oriented towards analyzing binary relations.We consider the question of generalizing link analysis methods for analyzing relational databases. To this aim, we provide a generalized ranking framework and address its practical implications.More specifically, we associate with each relational database and set of queries a unique weighted directed graph, which we call the database graph. We explore the properties of database graphs. In analogy to link analysis algorithms, which use the Web graph to rank web pages, we use the database graph to rank partial tuples. In this way we can, e.g., extend the PageRank link analysis algorithm to relational databases and give this extension a random querier interpretation.Similarly, we extend the HITS link analysis algorithm to relational databases. We conclude with some preliminary experimental results.

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PageRank, HITS and a Unified Framework for Link Analysis

Cited by 60 publications

References 1 publication

Recommendations of Personal Web Pages Based on User Navigational Patterns

Recommendations of Personal Web Pages Based on User Navigational Patterns

Mining significant semantic locations from GPS data

Relational link-based ranking

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