DBLP Research Articles

Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

During the past decades, the term “social computing” has become a promising interdisciplinary area in the intersection of computer science and social science. In this work, we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project (DBLP), a representative computer science bibliography website. We have observed a series of trends in the development of social computing, including the evolution of the number of publications, popular keywords, top venues, international collaborations, and research topics. Our findings will be helpful for researchers and practitioners working in relevant fields.

Serious Games for Executive Functions Training for Adults with Intellectual Disability: Overview.

(1) Background: Throughout the history of medical and psychology practice, specialists have worked to improve the quality of treatment and rehabilitation, which has led to the emergence of concepts such as serious games. These tools focus on different areas of intervention procedures, one of which is to help people with intellectual disability (ID). Individuals with ID have problems with executive functions (EFs), which are related to adaptive functioning. Recent studies showed that serious games positively impact cognitive, social, and communication skills in people with ID. The purpose of this study is to analyze the solutions that have been found in EF training for adults with ID in recent years, evaluating them with a number of key parameters and identifying the features and possible problems in the further development of our system. (2) Methods: A review was conducted starting with 573 articles in English related to serious games and selected from studies that had been published since 2015. Finally, 10 were examined in detail as they focused on EFs in adults with ID. They were searched in seven major databases (“Association for Computing Machinery” (ACM), IEEE Xplore database, DBLP computer science bibliography, Google Scholar, PubMed, SCOPUS, and PsycInfo). (3) Results: It was determined that the most frequent EFs referred to in the studies analyzed were planning and decision-making, followed by working memory and social cognition, behavioral regulation, flexibility, and inhibition capacity. The basic approach to the creation of support systems was also analyzed in terms of technical and program execution. The trend results’ analysis evidenced improvements in EFs, even though they were not significant. This comprehensive technique enabled the identification of the main features and aspects to be taken into account for further development of our system.

Developing a conceptual framework for short text categorization using hybrid CNN- LSTM based Caledonian crow optimization

Due to the continuous progression of social media networking sites, people share their thoughts, viewpoints, videos, speech and images through short texts. But the short texts are manually understandable but hard for the machine to collect data for clarification. The limited terms present in the short texts seem difficult while categorizing, analyzing as well as evaluating. Since social media and Bibliographic repositories like Digital Bibliography and Library Project (DBLP) contains a substantial amount of information, it is necessary to mine only useful information from the existing short texts. To achieve effectiveness and efficiency in short text categorization, the content-related characteristics derived from various machine learning techniques are admired. The significant objective of the proposed approach involves the categorization of short text and enhancing its accuracy, which can improve further finding collaborative research communities.. In this work, a hybrid convolutional neural network-long short term memory (CNN-LSTM) based new Caledonian crow optimization (NC2LO) model is utilized to classify the short texts. The crows utilize both social and asocial learning for developing tool modelling skills. Getting attracted to the behaviour of this variety of crows, and motivated by the learning strategy of crows, a new Caledonian crow optimization model referred to as NC2LO model is established. The conceptual framework of the proposed methodology consists of four different phases namely the data collection phase, data preparation phase, data pre-processing phase and short text categorization phase to classify the short texts with a high accuracy rate. Then, the proposed CNN-LSTM based NC2LO for short text categorization is evaluated using four different types of datasets namely IMDb, AG news, Twitter, and Tagmy News. Finally, the comparative analysis is carried out to evaluate the accuracy rate for various approaches and the analysis demonstrated that the proposed approach achieves a high accuracy rate of about 97%.

Self-adaptive systems: A systematic literature review across categories and domains

Championed by IBM’s vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences (SEAMS, SASO, ICAC) and workshops (SISSY) have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system — a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals despite changes in its environment. Despite a number of literature reviews on specific aspects of SASs ranging from their requirements to quality attributes, we lack a systematic understanding of the current state of the art. This paper contributes a systematic literature review into self-adaptive systems using the dblp computer science bibliography as a database. We filtered the records systematically in successive steps to arrive at 293 relevant papers. Each paper was critically analyzed and categorized into an attribute matrix. This matrix consisted of five categories, with each category having multiple attributes. The attributes of each paper, along with the summary of its contents formed the basis of the literature review that spanned 30 years (1990–2020). We characterize the maturation process of the research area from theoretical papers over practical implementations to more holistic and generic approaches, frameworks, and exemplars, applied to areas such as networking, web services, and robotics, with much of the recent work focusing on IoT and IaaS. While there is an ebb and flow of application domains, domains like bio-inspired approaches, security, and cyber–physical systems showed promise to grow heading into the 2020s.

Scientific success from the perspective of the strength of weak ties

We present the first complete verification of Granovetter’s theory of social networks using a massive dataset, i.e. DBLP computer science bibliography database. For this purpose, we study a coauthorship network, which is considered one of the most important examples that contradicts the universality of this theory. We achieve this goal by rejecting the assumption of the symmetry of social ties. Our approach is grounded in well-established heterogeneous (degree-based) mean-field theory commonly used to study dynamical processes on complex networks. Granovetter’s theory is based on two hypotheses that assign different roles to interpersonal, information-carrying connections. The first hypothesis states that strong ties carrying the majority of interaction events are located mainly within densely connected groups of people. The second hypothesis maintains that these groups are connected by sparse weak ties that are of vital importance for the diffusion of information—individuals who have access to weak ties have an advantage over those who do not. Given the scientific collaboration network, with strength of directed ties measured by the asymmetric fraction of joint publications, we show that scientific success is strongly correlated with the structure of a scientist’s collaboration network. First, among two scientists, with analogous achievements, the one with weaker ties tends to have the higher h-index, and second, teams connected by such ties create more cited publications.

Neural network technique with deep structure for improving author homonym and synonym classification in digital libraries

Author name disambiguation (AND), also recognized as name-identification, has long been seen as a challenging issue in bibliographic data. In other words, the same author may appear under separate names, synonyms, or distinct authors may have similar to those referred to as homonyms. Some previous research has proposed AND problem. To the best of our knowledge, no study discussed specifically synonym and homonym, whereas such cases are the core in AND topic. This paper presents the classification of non-homonym-synonym, homonym-synonym, synonym, and homonym cases by using the DBLP computer science bibliography dataset. Based on the DBLP raw data, the classification process is proposed by using deep neural networks (DNNs). In the classification process, the DBLP raw data divided into five features, including name, author, title, venue, and year. Twelve scenarios are designed with a different structure to validate and select the best model of DNNs. Furthermore, this paper is also compared DNNs with other classifiers, such as support vector machine (SVM) and decision tree. The results show DNNs outperform SVM and decision tree methods in all performance metrics. The DNNs performances with three hidden layers as the best model, achieve accuracy, sensitivity, specificity, precision, and F1-score are 98.85%, 95.95%, 99.26%, 94.80%, and 95.36%, respectively. In the future, DNNs are more performing with the automated feature representation in AND processing.

The aging effect in evolving scientific citation networks

The study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Traditional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.

Decision Support System for Solving Reviewer Assignment Problem

Reviewing is the most important step in the quality accreditation of scientific works, requires the professional expertise of the reviewer as well as there is no conflict of interest in the evaluation process. However, we also acknowledge that reviewers have limited knowledge, experience and opinions about the work of others, so they may misinterpret the author’s point of view, leading to the rejection of excellent scientific work or a potentially successful project proposal. Manually selecting reviewers can lead to bias and time-consuming. To solve these problems, we have developed a support system for selecting a group of reviewers to evaluate a particular problem, such as a proposal or a research paper. Our support system consists of three main modules: data collection, reviewer identification and group prediction of reviewers. The Data Collection module collects data from a variety of sources to create the scientist profile database. The reviewer identification module recognizes reviewers on a specific topic. The reviewer prediction module provides a group of exp reviewers to evaluate a submitted paper or a proposal. Experiments on the DBLP computer science bibliography dataset showed that our system achieves better results in terms of accuracy in comparison to other methods.

Conference Ranking based on Academic Network Embedding and Bipartite cross voting

Academic networks express the academic relationships among authors, papers and conferences, which offer perspectives and tools to rethink and solve traditional problems in the academic field. Among them, conference ranking is a problem which normally produced by collecting information from different sources and considering many factors. Not like most of the existed ranking methods that based on subjective evaluation, in this paper, we develop a Conference Ranking method based on Academic Network Embedding (CRANE) algorithm to simultaneously rank authors and conferences which improving the accuracy of ranking by embedding the Academic Network. The CRANE uses both conference features like internationality, citations and co-authors, and structure in Academic Network to evaluate the ranking of certain conferences. The theoretical analysis and convergence analysis were provided in this paper. Experiments on datasets of the Digital Bibliography & Library Project (DBLP) and AMiner academic platforms also be conducted. The experimental results show that the CRANE algorithm is superior to other algorithms by comparing with the public academic ranking like the China Computer Federation (CCF) ranking.

New prediction method for data spreading in social networks based on machine learning algorithm

Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.

Using Bibliometric Analysis to Measure and Understand the Gender Gap in Published Computing Books

The purpose of the research presented in this paper is the investigation of the gender gap in published computing books. The book titles from the DBLP computer science bibliography were the basis for this investigation. The conducted research involves co-authorship network exploration using social network analysis methods, as well as content learning by keyword extraction and ranking from book titles. The findings show that female authors tend to publish fewer books in computing than their male colleagues, and there is a huge gap of women regarding the collaboration. There are just two women names within the 50 author names with the highest social network top metrics, indicating collaboration. Regarding the extracted keywords, though there are differences, results do not show some huge divergences when it comes to the used language for computing titles.

Extraction of temporal relations from clinical free text: A systematic review of current approaches.

Temporal relations between clinical events play an important role in clinical assessment and decision making. Extracting such relations from free text data is a challenging task because it lies on between medical natural language processing, temporal representation and temporal reasoning. To survey existing methods for extracting temporal relations (TLINKs) between events from clinical free text in English; to establish the state-of-the-art in this field; and to identify outstanding methodological challenges. A systematic search in PubMed and the DBLP computer science bibliography was conducted for studies published between January 2006 and December 2018. The relevant studies were identified by examining the titles and abstracts. Then, the full text of selected studies was analyzed in depth and information were collected on TLINK tasks, TLINK types, data sources, features selection, methods used, and reported performance. A total of 2834 publications were identified for title and abstract screening. Of these publications, 51 studies were selected. Thirty-two studies used machine learning approaches, 15 studies used a hybrid approaches, and only four studies used a rule-based approach. The majority of studies use publicly available corpora: THYME (28 studies) and the i2b2 corpus (17 studies). The performance of TLINK extraction methods ranges widely depending on relation types and events (e.g. from 32% to 87% F-score for identifying relations between clinical events and document creation time). A small set of TLINKs (before, after, overlap and contains) has been widely studied with relatively good performance, whereas other types of TLINK (e.g., started by, finished by, precedes) are rarely studied and remain challenging. Machine learning classifiers (such as Support Vector Machine and Conditional Random Fields) and Deep Neural Networks were among the best performing methods for extracting TLINKs, but nearly all the work has been carried out and tested on two publicly available corpora only. The field would benefit from the availability of more publicly available, high-quality, annotated clinical text corpora.

An On-line Analytical Processing (OLAP) Aggregation Function for Rising Aspects in Collaboration and Social Networks

The overwhelming usage of social and collaboration networks provides the opportunity to analyze evolution of relationships among individuals, like celebrities or co-authors. Discovering such phenomenon in large complex networks is non-trivial due to their large sizes. In this situation, the aggregation functions used in OLAP, are useful to analyze the summarized data. OLAP has successfully proven its worth on multidimensional or complex networks. However, existing aggregations in the current OLAP systems do not produce versatile results in case of social and collaboration networks. This happens because said type of networks have structural connectivity/links among nodes, which cannot be considered by OLAP during its execution. In this situation, a useful discovery in terms of identifying pairs of nodes whose relationships is emerging in recent time, is missed. Such discovery of pairs of nodes is important for various applications such as targeted marketing, future joint partnerships and predicting future correspondence to name a few. In this study, we call such pairs as Rising_Pairs and propose an aggregation function for performing OLAP on network data whose historical information is maintained over a period of times. Using structural information, Rising_Pairs, our proposed aggregation function, discovers the strongly coupled pairs in a network data by emphasizing their recent interactions and attribute similarities. In this way, useful information related to strongly coupled pairs in a network is identified. To verify the effectiveness of our proposal, we implemented it on various types of real-world networks like Facebook, Digital Bibliography and Library Project (DBLP) and Global Positioning System (GPS) trajectory datasets and observed interesting patterns.

Empirical Performace Determination on Community Detection Techniques in Social Networks

Community identification is the high common and extending field of interest in social and real-time network applications. In recent years, many community detection methods have been developed. This paper describes various community discovery methods such as InfoMap, Clique Guided, Louvain, Newman and Eigen Vector that have already been developed and also compares the experimental results of those proposed techniques. The proposed work in this paper experiments these community mining algorithms on the two real-world datasets Twitter and DBLP (Computer Science Bibliography) networks. The identified communities by all the community mining algorithms for these two data sets are described in this proposed work. The quality of the derived communities is evaluated by using standard Extended Modularity metric. The experiment results show that the InfoMap algorithm produces a good modularity score than other community mining algorithms for different sizes of communities on both data sets.

Graft Rejection Prediction Following Kidney Transplantation Using Machine Learning Techniques: A Systematic Review and Meta-Analysis.

Kidney transplantation is recommended for patients with End-Stage Renal Disease (ESRD). However, complications, such as graft rejection are hard to predict due to donor and recipient variability. This study discusses the role of machine learning (ML) in predicting graft rejection following kidney transplantation, by reviewing the available related literature. PubMed, DBLP, and Scopus databases were searched to identify studies that utilized ML methods, in predicting outcome following kidney transplants. Fourteen studies were included. This study reviewed the deployment of ML in 109,317 kidney transplant patients from 14 studies. We extracted five different ML algorithms from reviewed studies. Decision Tree (DT) algorithms revealed slightly higher performance with overall mean Area Under the Curve (AUC) for DT (79.5% ± 0.06) was higher than Artificial Neural Network (ANN) (78.2% ± 0.08). For predicting graft rejection, ANN and DT were at the top among ML models that had higher accuracy and AUC.

Scholars\u2019 Perceptions of Relevance in Bibliography-Based People Recommender System

Collaboration and social networking are increasingly important for academics, yet identifying relevant collaborators requires remarkable effort. While there are various networking services optimized for seeking similarities between the users, the scholarly motive of producing new knowledge calls for assistance in identifying people with complementary qualities. However, there is little empirical understanding of how academics perceive relevance, complementarity, and diversity of individuals in their profession and how these concepts can be optimally embedded in social matching systems. This paper aims to support the development of diversity-enhancing people recommender systems by exploring senior researchers’ perceptions of recommended other scholars at different levels on a similar–different continuum. To conduct the study, we built a recommender system based on topic modeling of scholars’ publications in the DBLP computer science bibliography. A study of 18 senior researchers comprised a controlled experiment and semi-structured interviewing, focusing on their subjective perceptions regarding relevance, similarity, and familiarity of the given recommendations, as well as participants’ readiness to interact with the recommended people. The study implies that the homophily bias (behavioral tendency to select similar others) is strong despite the recognized need for complementarity. While the experiment indicated consistent and significant differences between the perceived relevance of most similar vs. other levels, the interview results imply that the evaluation of the relevance of people recommendations is complex and multifaceted. Despite the inherent bias in selection, the participants could identify highly interesting collaboration opportunities on all levels of similarity.

On relationship formation in heterogeneous information networks: An inferring method based on multilabel learning

This paper studies how relationships form in heterogeneous information networks (HINs). The objective is not only to predict relationships in a given HIN more accurately but also to discover the interdependency between different type of relationships. A new relationship prediction method MULRP based on multilabel learning (MLL in brief) is proposed. In MULRP, the types of relationship between two nodes are represented by the meta‐paths between nodes and each type of relationship is given a label. Under the framework of MLL, any potential relationships including the target relationship can be predicted. Moreover, the method can output the reasonable dependency scores between relationships. Thus, more viable paths will be provided to facilitate the formation of new relationships. The proposed method is evaluated on two real datasets: The DBLP Computer Science Bibliography(abbr. DBLP) network and Twitter network. The experimental results show that by using heterogeneous information in a supervised MLL setting, MULRP achieves better performance in comparison to several baseline binary classification methods and a state‐of‐art relationship prediction method.

Speeding DBLP querying using hadoop and spark

Big data is becoming bigger every day. Even for simple applications such as the Digital Bibliography & Library Project (DBLP) database, the data is becoming unmanageable using the conventional databases because of its size. Applying big data processing methods such as Hadoop and Spark is becoming more popular because of that. In this work, we investigate the use of Hadoop and Spark in the querying process of big data and we compare the performance of them in terms of their execution time. We use the DBLP database as a case study. Results show that Hadoop and Spark enhances the query execution time significantly when compared with conventional database management systems. We also found that Spark enhances the execution time over Hadoop.

Ranking Authors in an Academic Network Using Social Network Measures

Online social networks are widely used platforms that enable people to connect with each other. These social media channels provide an active communication platform for people, and they have opened new venues of research for the academic world and business. One of these research areas is measuring the influential users in online social networks; and the same is true for academic networks where finding influential authors is an area of interest. In an academic network, citation count, h-index and their variations are used to find top authors. In this article, we propose the adoption of established social network measures, including centrality and prestige, in an academic network to compute the rank of authors. For the empirical analysis, the widely-used dataset of the Digital Bibliography and Library Project (DBLP) is exploited in this research, and the micro-level properties of the network formed in the DBLP co-authorship network are studied. Afterwards, the results are computed using social network measures and evaluated using the standard ranking performance evaluation measures, including Kendall correlation, Overlapping Similarlity (OSim) and Spearman rank-order correlation. The results reveal that the centrality measures are significantly correlated with the citation count and h-index. Consequently, social network measures have potential to be used in an academic network to rank the authors.

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review.

BackgroundSeveral studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published.ObjectiveThe objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies.MethodsWe conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017.ResultsA total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity).ConclusionsSeveral studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.