Identifying real world persons by their name is a significant problem, especially for digital libraries like DBLP. Though there are a large number of algorithmic approaches, finding and correcting name-related inconsistencies is time-consuming and expensive. We introduce an extension to the DBLP collection which allows us to mine for modifications to name entities in a period of ten years. We use our findings to analyze how defective entities integrated into different dynamic social networks. Based on first results which showed that name errors are unevenly distributed in these networks we present and evaluate an approach to identify areas which are prone to name inconsistencies and require a more extensive monitoring. View full abstract»

A Search Support Engine (SSE) is implemented based on the basic principles of Information Retrieval Support Systems (IRSS) and Information Seeking Support Systems (ISSS). An SSE aims at meeting the diversity needs from different users, providing various supporting functionalities, tools, etc. for users to perform various tasks beyond the traditional search and browsing provided by current search engines. As an illustrative example, we developed a DBLP search support engine (DBLP-SSE), and we discuss some concrete supporting functionalities, namely, search refinement support, domain analysis support, etc. Each of the functionality focus on a unique perspective supporting users finding useful information and knowledge from the DBLP dataset. The search support engine can be considered as a step towards Knowledge Retrieval (KR) and Web Intelligence (WI). View full abstract»

In this paper, we construct a word association network from DBLP bibliography records and detect its evolution progress based on community discovery algorithm CPM and CPMw. We found that the network is of complex network characteristics, and the detected semantic communities can be classified into two categories: giant community and small community. They differ in size and content, and behave differently in the network evolution. Discovering the evolution of network and the emergent semantic communities can help researchers grasp the state of arts of the related field, identify emergent issues and thus inspire new idea to solve scientific questions. View full abstract»

Finding relevant experts in a specific field is often crucial for consulting, both in industry and in academia. The aim of this paper is to address the expert-finding task in a real world academic field. We present three models for expert finding based on the large-scale DBLP bibliography and Google scholar for data supplementation. The first, a novel weighted language model, models an expert candidate based on the relevance and importance of associated documents by introducing a document prior probability, and achieves much better results than the basic language model. The second, a topic-based model, represents each candidate as a weighted sum of multiple topics, whilst the third, a hybrid model, combines the language model and the topic-based model. We evaluate our system using a benchmark dataset based on human relevance judgments of how well the expertise of proposed experts matches a query topic. Evaluation results show that our hybrid model outperforms other models in nearly all metrics. View full abstract»

In the article there is presented comparison of overlapping clustering methods for data mining of DBLP datasets. For the analysis, the DBLP data sets were pre-processed, while each journal has been assigned attributes, defined by its topics. The data collection can be described as vague and uncertain; obtained clusters and applied queries do not necessarily have crisp boundaries. The authors presented clustering through a tolerance rough set method (TRSM) and fuzzy c-mean (FCM) algorithm for journal recommendation based on topic search. The comparison of both clustering methods was presented using different measures of similarity. View full abstract»

Studies shows that finding frequent sub-graphs in uncertain graphs database is an NP complete problem. Finding the frequency at which these sub-graphs occur in uncertain graph database is also computationally expensive. This paper focus on investigation of mining frequent sub-graph patterns in DBLP uncertain graph data using an approximation based method. The frequent sub-graph pattern mining problem is formalized by using the expected support measure. Here n approximate mining algorithm based Weighted MUSE, is proposed to discover possible frequent sub-graph patterns from uncertain graph data. View full abstract»

In this paper we propose a new perspective for the data analysis in digital libraries, bibliographic and other databases containing personal names. Knowing language/cultural background of a person can be beneficial in many applications, however this information is often not present explicitly in the databases. We present here a statistical tool for the automatic language detection of personal names. Our system does not require a dictionary of names for training and handles 14 different languages so far. General purpose corpora for all Western European, Chinese, Japanese and Turkish languages are used in order to build simple statistical models of the languages. The tool is fine tuned to achieve precision and recall above 90% for many languages which proves better performance than some other systems aiming at the language identification of personal names. On an example of a bibliographical database DBLP we show how our tool can be used in tasks such as data cleaning and discovery of trends. View full abstract»

This paper presents an online analysis tool called Forcoa.NET, which is built over the DBLP dataset of publications from the field of computer science. The developed tool is focused on the analysis and visualization of the co-authorship relationship based on the intensity and topic of joint publications. The visualization of co-authorship networks allows to describe the author and his/her current surroundings while still incorporating the historical aspect. The analysis is based on using the forgetting function to hold the information relevant to the selected date. After this analysis, we are capable of computing several measures, which can describe different aspects of user behaviour from the point of view of scientific social network. View full abstract»

With the increasing popularity of data-centric XML, data warehousing and mining applications are being developed for rapidly burgeoning XML data repositories. Data quality will no doubt be a critical factor for the success of such applications. Data cleaning, which refers to the processes used to improve data quality, has been well researched in the context of traditional databases. In earlier work we developed a knowledge-based framework for data cleaning relational databases. In this work, we present a novel attempt to apply this framework to XML databases. Our experimental dataset is the DBLP database, a popular online XML bibliography database used by many researchers View full abstract»

DBLP is a bibliographic database with more than one million data entries, collected from the last 70 years, and labeled with diverse attributes like the authorspsila names, the publication title, and the year of publishing. With this as ground, the motivation of applying analytical examinations to identifying publishing communities become meaningful. In this respect and focusing on the idea of figuring out existing associative connectivity between authors, this work exposes some novel information as for example the most frequent community patterns, the ldquoDonald E. Ullmanrdquo-star of 1975, and an example for a typical extreme-sized community. We close with a temporal flight throughout the decades while observing the extreme-sized community and highlight perspectives and further analytical suggestions. View full abstract»

The cell loss probability is an important consideration in ATM switch technology. We provide an analysis of the cells loss in a switch and the faults of the buffered leaky policing (BLP) algorithm which is used to solve outline congestion, propose a new algorithm by introducing the concept of priority control which is also analysed. We also discuss the full utilization of the first class buffer, and demonstrate and calculate the dynamic leaky policing (DBLP) algorithm by building an input traffic model. Finally we solve the contradiction between the delay time and cell loss probability. The DBLP algorithm has the function of traffic smoothing and control. It is a good method that is suitable for bursty traffic control View full abstract»

This study introduces a new soft computing method for expert identification in social networks based on formal concept analysis and fuzzy rules. Expert identification is an important task in social network analysis and there are several methods to identify people who have experience in given area. In this paper, we propose a hybrid approach where the formal concept analysis is used for finding author's profiles based on keywords and fuzzy rules to learn the properties of the authors and to enhance the set of experts. View full abstract»

In this paper, we describe idea-map, a mash-up application building on top of Linked Data Cloud. It reads in user's keywords (about research ideas) and executes a SPARQL query against DBLP endpoint. Spatial and temporal information is extracted and parsed from the query results and is further transformed to SIMILE/EXHIBIT to show a spatiotemporal map for the research ideas. Idea-map shows the feasibility of combing various techniques such as YQL, SIMILE/Exhibit and SPARQL query answering to provide an insightful interface to better understand the interested research ideas. View full abstract»

A name is a key feature for distinguishing people, but we often fail to distinguish people because an author may have multiple names or multiple authors may share the same name. Such name ambiguity problems affect the performance of the document retrieval, web search and database integration. Especially, in bibliographic information, a number of errors may be included since there are different authors with the same name or an author name may be misspelled or represented with an abbreviation. For solving these problems, it is necessary to disambiguate the names inputted into the database. In this paper, we propose a method to solve the name ambiguity by using social networks constructed based on the relations among authors. We evaluated the effectiveness of the proposed system based on the DBLP data that offer computer science bibliographic information. View full abstract»

One of the most obvious features of social networks is their community structure. Several types of methods were developed for discovering communities in the networks, either from the global perspective or based on local information only. Local methods are appropriate when working with large and dynamic networks or when real-time results are expected. In this paper we explore two such methods and compare the results obtained on the sample of a co-authorship network. We study how much may detected communities vary according to the method used for computation. View full abstract»

Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures and mine data from real world networks. In this paper, we use a simple spectral decomposition to analyze a co-authorship network. We use a straightforward approach based on algebraic connectivity and characteristic valuation and show that even this simple form of spectral partitioning is useful for the analysis of relations and communities in a co-authorship networks. View full abstract»

Some large scale topical digital libraries, such as CiteSeer, harvest online academic documents by crawling open-access archives, university and author homepages, and authors' self-submissions. While these approaches have so far built reasonable size libraries, they can suffer from having only a portion of the documents from specific publishing venues. We propose to use alternative online resources and techniques that maximally exploit other resources to build the complete document collection of any given publication venue. We investigate the feasibility of using publication metadata to guide the crawler towards authors' homepages to harvest what is missing from a digital library collection. We collect a real-world dataset from two Computer Science publishing venues, involving a total of 593 unique authors over a time frame of 1998 to 2004. We then identify the missing papers that are not indexed by CiteSeer. Using a fully automatic heuristic-based system that has the capability of locating authors' homepages and then using focused crawling to download the desired papers, we demonstrate that it is practical to harvest using a focused crawler academic papers that are missing from our digital library. Our harvester achieves a performance with an average recall level of 0.82 overall and 0.75 for those missing documents. Evaluation of the crawler's performance based on the harvest rate shows definite advantages over other crawling approaches and consistently outperforms a defined baseline crawler on a number of measures View full abstract»

Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures in real world networks and databases. In this paper, we use the spectral partitioning to detect communities in a co-authorship network. The partitioning depends heavily on the weighting of the underlying network. We use an intuitive weighting scheme based on the ant colony optimization and show the communities found by spectral partitioning when using the ACO inspired weighting and when using trivial weighting based on the number of interactions between the authors. View full abstract»

Social networks have been evolving over the past few years, leading to a rapid increase in the number and complexity of relationships among their entities. In this paper, we focus on a large scale dataset known as the Digital Bibliography and Library Project (DBLP), which contains information on all publications that have been published in computer and information science related journals and conference proceedings. We model the DBLP dataset as a social network of research collaborations. DBLP is a structured and dynamic dataset stored in the XML file format; it contains over 850,000 authors and 2 million publications and the resulting collaboration social network is a scale-free network. We define DBLP collaboration social network as a graph that consists of researchers as nodes and links representing the collaboration among the researchers. In this work, we implement a data analysis algorithm called Multidimensional Scaling (MDS) to represent the degree of collaboration among the DBLP authors as Euclidean distances in order to analyze, mine and understand the relational information in this large scale network in a visual way. MDS requires a highly computational complexity for large scale graphs such as the DBLP graph. Therefore, we propose different solutions to overcome this problem, and improve the MDS performance. In addition, as the quality of the MDS result is measured by a metric known as the stress value, we use the steepest descent method to minimize the stress in an iterative process called stress optimization in order to generate the best geometric layout of the graph. We also propose a solution to further enhance the graph visualization by partitioning the graph into sub-graphs and using repelling forces among nodes within the same sub-graph. View full abstract»

Collaboration networks arise when we map the connections between scientists which are formed through joint publications. These networks thus display the social structure of academia, and also allow conclusions about the structure of scientific knowledge. Using the computer science publication database DBLP, we compile relations between authors and publications as graphs and proceed with examining and quantifying collaborative relations with graph-based methods. We review standard properties of the network and rank authors and publications by centrality. Additionally, we detect communities with modularity-based clustering and compare the resulting clusters to a ground-truth based on conferences and thus topical similarity. In a second part, we are the first to combine DBLP network data with data from the Dagstuhl Seminars: We investigate whether seminars of this kind, as social and academic events designed to connect researchers, leave a visible track in the structure of the collaboration network. Our results suggest that such single events are not influential enough to change the network structure significantly. However, the network structure seems to influence a participant's decision to accept or decline an invitation. View full abstract»

Name disambiguation is a focal point on realworld information integration, analysis, and data mining. This problem, also known as the classical name authority control problem, consists in "same authors with different spellings" or "different authors with the same spelling". The problem is augmented in large data repositories where information changes and grows over time (e.g., DBLP, CiteSeer). In particular, we are mainly interested in DBLP because we use this database to discover the publishing movement among Mexican researchers. In this paper, we propose an algorithm that solves the name authority control problem. Our approach aims to improve the identity author tracking by using semantic information about authors, even thought they use different name varieties to sign their research work over time. View full abstract»

This paper introduces an approach to derive whether an individual is related to an item or not. In our approach, the well-known DBLP dataset is used and we try to find some skills that are related to an author that we were not aware of before. To realize our objective, we cluster authors and skills using Spectral Graph Clustering algorithm, then simultaneously obtain user and movie clusters via Bipartite Graph (Bigraph) Spectral Co-clustering approach, and then generate predictions based on the outputs of clustering and co-clustering steps. Accordingly, we utilize clustering and co-clustering advantages to predict the probability of link existing between an author and a skill. Experimental results on DBLP dataset show that our approach works well in the specified task. View full abstract»

DeaiExplorer uses RFID technology to dynamically derive interconnected social clusters from a publication database. It reveals these social networks on a display, letting colocated conference participants discover interpersonal connections. DeaiExplorer communicates with RFID tags attached to participants' conference badges and visualizes their mutual connections derived from the DBLP. The system scans RFID tags and uses participants' names to retrieve records in the DBLP database. Participants can either passively view the displayed networks or actively explore the networks through interactive zooming and scrolling View full abstract»

Most previous actor-node ranking algorithms for event-based social networks only consider how many events an actor participates in. However in event-based social networks, we should also consider the influence of events when we rank actor-nodes. In this paper we formally define event-based social networks and related concepts, then we propose rules to construct an event-based social network. Algorithms are presented to discover the activity and importance of each actor-node. We test the algorithms by analysing the DBLP data set. In the experiment actors in DBLP data set are ranked based on their activity, importance, and combination of activity and importance, respectively. View full abstract»

Researchers have discovered, in recent years, the advantages of modeling complex systems using heterogeneous information networks. These networks are comprised of heterogeneous sets of nodes and edges that better represent the different entities and relationships often found in the real world. Although heterogeneous networks provide a richer semantic view of the data, the added complexity makes it difficult to directly apply existing techniques that work well on homogeneous networks. In this paper, we propose a graph modification process that alters an existing heterogeneous bibliographic network into another network, with the purpose of highlighting the important relations in the bibliographic network. Several importance scores, some adopted from existing work and others defined in this work, are then used to measure the importance of links in the modified network. The link prediction problem is studied on the modified network by implementing a random walk-based algorithm on the network. The importance scores and the structure of the modified graph are used to guide a random walker towards relevant parts of the graph, i.e. towards nodes to which new links will be created in the future. The different properties of the proposed algorithm are evaluated experimentally on a real world bibliographic network, the DBLP. Results show that the proposed method outperforms the state-of-the-art supervised technique as well as various approaches based on topology and node attributes. View full abstract»