Citation Graph Research Articles

Document Similarity Search Algorithm Based On Hierarchy Model

Searching for similar documents from huge amounts of documents is an important and time consuming problem. Although the numerous precise models have been developed for the task, the traditional search algorithms are unable to meet the needs of users for quick search. Herein, a new document similarity calculation and search method with high efficiency is proposed. The calculation of the similarity is based on the total probability model and the efficient search is achieved via level n nodes and paths of citation graph. A special approach from the branch and bound limits the search scope and provide decision algorithm. With the increase in the number of documents, the efficiency of the proposed algorithm is dramatically promoted.

Diversifying Citation Recommendations

Literature search is one of the most important steps of academic research. With more than 100,000 papers published each year just in computer science, performing a complete literature search becomes a Herculean task. Some of the existing approaches and tools for literature search cannot compete with the characteristics of today’s literature, and they suffer from ambiguity and homonymy. Techniques based on citation information are more robust to the mentioned issues. Thus, we recently built a Web service called the advisor, which provides personalized recommendations to researchers based on their papers of interest. Since most recommendation methods may return redundant results, diversifying the results of the search process is necessary to increase the amount of information that one can reach via an automated search. This article targets the problem of result diversification in citation-based bibliographic search, assuming that the citation graph itself is the only information available and no categories or intents are known. The contribution of this work is threefold. We survey various random walk--based diversification methods and enhance them with the direction awareness property to allow users to reach either old, foundational (possibly well-cited and well-known) research papers or recent (most likely less-known) ones. Next, we propose a set of novel algorithms based on vertex selection and query refinement. A set of experiments with various evaluation criteria shows that the proposed γ-RLM algorithm performs better than the existing approaches and is suitable for real-time bibliographic search in practice.

Inheritance Patterns in Citation Networks Reveal Scientific Memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and we validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

Patent Query Formulation by Synthesizing Multiple Sources of Relevance Evidence

Patent prior art search is a task in patent retrieval with the goal of finding documents which describe prior art work related to a query patent. A query patent is a full patent application composed of hundreds of terms which does not represent a single focused information need. Fortunately, other relevance evidence sources (i.e., classification tags and bibliographical data) provide additional details about the underlying information need. In this article, we propose a unified framework that integrates multiple relevance evidence components for query formulation. We first build a query model from the textual fields of a query patent. To overcome the term mismatch, we expand this initial query model with the term distribution of documents in the citation graph, modeling old and recent domain terminology. We build an IPC lexicon and perform query expansion using this lexicon incorporating proximity information. We performed an empirical evaluation on two patent datasets. Our results show that employing the temporal features of documents has a precision enhancing effect, while query expansion using IPC lexicon improves the recall of the final rank list.

Exploiting citation networks for large-scale author name disambiguation

We present a novel algorithm and validation method for disambiguating author names in very large bibliographic data sets and apply it to the full Web of Science (WoS) citation index. Our algorithm relies only upon the author and citation graphs available for the whole period covered by the WoS. A pair-wise publication similarity metric, which is based on common co-authors, self-citations, shared references and citations, is established to perform a two-step agglomerative clustering that first connects individual papers and then merges similar clusters. This parameterized model is optimized using an h-index based recall measure, favoring the correct assignment of well-cited publications, and a name-initials-based precision using WoS metadata and cross-referenced Google Scholar profiles. Despite the use of limited metadata, we reach a recall of 87% and a precision of 88% with a preference for researchers with high h-index values. 47 million articles of WoS can be disambiguated on a single machine in less than a day. We develop an h-index distribution model, confirming that the prediction is in excellent agreement with the empirical data, and yielding insight into the utility of the h-index in real academic ranking scenarios.

GraMi

Mining frequent subgraphs is an important operation on graphs; it is defined as finding all subgraphs that appear frequently in a database according to a given frequency threshold. Most existing work assumes a database of many small graphs, but modern applications, such as social networks, citation graphs, or protein-protein interactions in bioinformatics, are modeled as a single large graph. In this paper we present GraMi, a novel framework for frequent subgraph mining in a single large graph. GraMi undertakes a novel approach that only finds the minimal set of instances to satisfy the frequency threshold and avoids the costly enumeration of all instances required by previous approaches. We accompany our approach with a heuristic and optimizations that significantly improve performance. Additionally, we present an extension of GraMi that mines frequent patterns. Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Finally, we present CGraMi, a version supporting structural and semantic constraints, and AGraMi, an approximate version producing results with no false positives. Our experiments on real data demonstrate that our framework is up to 2 orders of magnitude faster and discovers more interesting patterns than existing approaches.

Inheritance Patterns in Citation Networks Reveal Scientific Memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and we validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

Reverse top-k search using random walk with restart

With the increasing popularity of social networks, large volumes of graph data are becoming available. Large graphs are also derived by structure extraction from relational, text, or scientific data (e.g., relational tuple networks, citation graphs, ontology networks, protein-protein interaction graphs). Node-to-node proximity is the key building block for many graph-based applications that search or analyze the data. Among various proximity measures, random walk with restart (RWR) is widely adopted because of its ability to consider the global structure of the whole network. Although RWR-based similarity search has been well studied before, there is no prior work on reverse top- k proximity search in graphs based on RWR. We discuss the applicability of this query and show that its direct evaluation using existing methods on RWR-based similarity search has very high computational and storage demands. To address this issue, we propose an indexing technique, paired with an on-line reverse top- k search algorithm. Our experiments show that our technique is efficient and has manageable storage requirements even when applied on very large graphs.

Review of the indirect citations paradigm: theory and practice of the assessment of papers, authors and journals

The family of indicators presented in this paper includes indices created by taking into account not only the direct but also the indirect impact of citations and references. Three types of citation graphs are presented, namely, the Paper-Citation graph, the Author-Citation graph and the Journal-Citation graph, along with different methods for constructing them. In addition, the concept of generations of citations is examined in detail, again by presenting various methods for defining them found in the literature. Finally, a number of indirect indicators for papers, authors and journals are discussed, which among others, include PageRank, CiteRank, indirect h-index and the EigenFactor score.

The effect of citation analysis on query expansion for patent retrieval

Patent prior art search is a type of search in the patent domain where documents are searched for that describe the work previously carried out related to a patent application. The goal of this search is to check whether the idea in the patent application is novel. Vocabulary mismatch is one of the main problems of patent retrieval which results in low retrievability of similar documents for a given patent application. In this paper we show how the term distribution of the cited documents in an initially retrieved ranked list can be used to address the vocabulary mismatch. We propose a method for query modeling estimation which utilizes the citation links in a pseudo relevance feedback set. We first build a topic dependent citation graph, starting from the initially retrieved set of feedback documents and utilizing citation links of feedback documents to expand the set. We identify the important documents in the topic dependent citation graph using a citation analysis measure. We then use the term distribution of the documents in the citation graph to estimate a query model by identifying the distinguishing terms and their respective weights. We then use these terms to expand our original query. We use CLEF-IP 2011 collection to evaluate the effectiveness of our query modeling approach for prior art search. We also study the influence of different parameters on the performance of the proposed method. The experimental results demonstrate that the proposed approach significantly improves the recall over a state-of-the-art baseline which uses the link-based structure of the citation graph but not the term distribution of the cited documents.

Full‐text citation analysis: A new method to enhance scholarly networks

In this article, we use innovative full‐text citation analysis along with supervised topic modeling and network‐analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full‐text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author‐contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be “important publications” for the target topic (keyword), and we use these cited publications to validate our topic‐ranking result and to compare different publication‐ranking lists. Evaluation results show that full‐text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency–inverted document frequency (tf–idf), language model, BM25, PageRank, and PageRank + language model (p < .001), for academic information retrieval (IR) systems.

Fast recommendation on bibliographic networks with sparse-matrix ordering and partitioning

Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparse-matrix computation which arises in our publicly available citation recommendation service theadvisor as well as in many other recommendation systems. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and we reduced the number of cache misses with ordering and partitioning. Experimental results show that our techniques are highly efficient in reducing the query processing time which is highly crucial for a web service.

The Searchbench - Combining Sentence-semantic, Full-text and Bibliographic Search in Digital Libraries

We describe a novel approach to precise searching in the full content of digital libraries. The Searchbench (for search workbench) is based on sentence-wise syntactic and semantic natural language processing (NLP) of both born-digital and scanned publications in PDF format. The term born-digital means natively digital, i.e. prepared electronically using typesetting systems such as LaTeX, OpenOffice, and the like. In the Searchbench, queries can be formulated as (possibly underspecified) statements, consisting of simple subject-predicate-object constructs such as ‘algorithm improves word alignment’. This reduces the number of false hits in large document collections when the search words happen to appear close to each other, but are not semantically related. The method also abstracts from passive voice and predicate synonyms. Moreover, negated statements can be excluded from the search results, and negated antonym predicates again count as synonyms (e.g. not include = exclude).In the Searchbench, a sentence-semantic search can be combined with search filters for classical full-text, bibliographic metadata and automatically computed domain terms. Auto-suggest fields facilitate text input. Queries can be bookmarked or emailed. Furthermore, a novel citation browser in the Searchbench allows graphical navigation in citation networks. These have been extracted automatically from metadata and paper texts. The citation browser displays short phrases from citation sentences at the edges in the citation graph and thus allows students and researchers to quickly browse publications and immerse into a new research field. By clicking on a citation edge, the original citation sentence is shown in context, and optionally also in the original PDF layout.To showcase the usefulness of our research, we have a applied it to a collection of currently approx. 25,000 open access research papers in the field of computational linguistics and language technology, the ACL Anthology ( http://aclweb.org/anthology). The Searchbench user interface is a web application running in every modern, JavaScript-enabled web browser, also on smart phones and tablet computers. The system is a free and public service at http://aclasb.dfki.de. Because the NLP technology is domain-independent, it could also be applied to newspaper texts, technical documentation, or scientific publications from other disciplines. The aim of this paper is to make the benefits of this new, language technology based approach known in library research and related fields.This article summarises 9 peer reviewed publications from the past three years that have been published in international conferences and workshops in the area of computational linguistics, and tries to present them in an appropriate way to the LIBER audience. The original papers contain more details and are freely available from the author’s homepage[1] or via the Searchbench[2].

Задачи и методы автоматического построения графа цитирований по коллекции научных документов

Задачи и методы автоматического построения графа цитирований по коллекции научных документов

Geometric graph properties of the spatial preferred attachment model

The spatial preferred attachment (SPA) model is a model for networked information spaces such as domains of the World Wide Web, citation graphs, and on-line social networks. It uses a metric space to model the hidden attributes of the vertices. Thus, vertices are elements of a metric space, and link formation depends on the metric distance between vertices. We show, through theoretical analysis and simulation, that for graphs formed according to the SPA model it is possible to infer the metric distance between vertices from the link structure of the graph. Precisely, the estimate is based on the number of common neighbours of a pair of vertices, a measure known as co-citation. To be able to calculate this estimate, we derive a precise relation between the number of common neighbours and metric distance. We also analyse the distribution of edge lengths, where the length of an edge is the metric distance between its end points. We show that this distribution has three different regimes, and that the tail of this distribution follows a power law.

Universality of performance indicators based on citation and reference counts

We find evidence for the universality of two relative bibliometric indicators of the quality of individual scientific publications taken from different data sets. One of these is a new index that considers both citation and reference counts. We demonstrate this universality for relatively well cited publications from a single institute, grouped by year of publication and by faculty or by department. We show similar behaviour in publications submitted to the arXiv e-print archive, grouped by year of submission and by sub-archive. We also find that for reasonably well cited papers this distribution is well fitted by a lognormal with a variance of around σ2 = 1.3 which is consistent with the results of Radicchi et al. (Proc Natl Acad Sci USA 105:17268–17272, 2008). Our work demonstrates that comparisons can be made between publications from different disciplines and publication dates, regardless of their citation count and without expensive access to the whole world-wide citation graph. Further, it shows that averages of the logarithm of such relative bibliometric indices deal with the issue of long tails and avoid the need for statistics based on lengthy ranking procedures.

Citation genetic genealogy: a novel insight for citation analysis in scientific literature

Citation relationships are commonly described with citation network or citation graph, but in this article, the author introduced the notion of citation genetic genealogy and apply it in citation analysis. A citing document usually only uses pieces of its cited document, so the author of this article defined these valuable pieces of a scientific document, which carry the information that have been used by its citing documents as its document genes. Besides, with the definition of symbolic information of a scientific document, the conclusion that a citing document inherited the document genes from its references can be drawn. Based on these understandings, citation genetic genealogy is constructed to describe citation relationships. With citation genetic genealogy, it is easy to map the citation relationships, like bibliographic coupling and co-citation, with familiar family relationships and illustrate the inheritance relationships in scientific literatures. Also, citation genetic genealogy may provide an interface between the citation analysis of a document set and the content analysis for each individual document inside this document set.

Mining citation information from CiteSeer data

The CiteSeer digital library is a useful source of bibliographic information. It allows for retrieving citations, co-authorships, addresses, and affiliations of authors and publications. In spite of this, it has been relatively rarely used for automated citation analyses. This article describes our findings after extensively mining from the CiteSeer data. We explored citations between authors and determined rankings of influential scientists using various evaluation methods including citation and in-degree counts, HITS, PageRank, and its variations based on both the citation and collaboration graphs. We compare the resulting rankings with lists of computer science award winners and find out that award recipients are almost always ranked high. We conclude that CiteSeer is a valuable, yet not fully appreciated, repository of citation data and is appropriate for testing novel bibliometric methods.

F-Value: measuring an article’s scientific impact

The f-value is a new indicator that measures the importance of a research article by taking into account all citations received, directly and indirectly, up to depth n. The f-value considers all information present in a Citation Graph in order to produce a ranking of the articles. Apart from the mathematical equation that calculates the f-value, we also present the corresponding algorithm with its implementation, plus an experimental comparison of f-value with two known indicators of an article's scientific importance, namely, the number of citations and the Page Rank for citation analysis. Finally, we discuss the similarities and differences among the indicators.

An approach based on citation analysis to support effective handling of regulatory compliance

For most global software companies with a client base that covers a large number of regulated businesses, regulatory compliance represents a significant challenge. The world of compliance has become increasingly complex due to the overwhelming number of regulations, laws, and standards that are introduced every year. These laws may vary significantly in their scope and applicability depending on the industry sector and the geographical area of the end client. In addition, many of these laws are created by different legislative bodies resulting in overlapping and sometimes conflicting provisions. To further complicate matters, laws are often created based on existing ones, forming a complex set of interdependent rules where changes made in one place can propagate to affect, sometimes in an inconsistent manner, many other laws. There is clearly a need to investigate techniques and tools that can alleviate IT solution providers from the complexity of dealing with regulatory compliance. In this paper, we present an approach and a supporting tool that aim to facilitate the analysis of multiple regulations. Our approach is based on the exploration of the citation relationship that links various laws together. The citation relationship is represented by a citation graph that can be used by an analyst to navigate through the provisions of various interrelated laws to uncover overlaps and possible conflicts or to simply understand the content of specific law documents. We also present a tool called CompDSS (Compliance Decision Support System) that supports our approach. Finally, we show the effectiveness of the presented approach by applying it to three regulations, namely, SOX, HIPAA, and GLBA.