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دسترسی آزاد مقاله
1 - Representing a Content-based link Prediction Algorithm in Scientific Social Networks
Hosna Solaimannezhad omid fatemiPredicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. A چکیده کاملPredicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. As a part of recent improvements of research, far much attention is devoted to the computational analysis of these social networks. The dynamics of these networks makes them challenging to study. Link prediction is one of the main problems in social networks analysis. If we represent a social network with a graph, link prediction means predicting edges that will be created between nodes in the future. The output of link prediction algorithms is using in the various areas such as recommender systems. Also, collaboration prediction between two authors using their research interests is one of the issues that improve group researches. There are few studies on link prediction that use content published by nodes for predicting collaboration between them. In this study, a new link prediction algorithm is developed based on the people interests. By extracting fields that authors have worked on them via analyzing papers published by them, this algorithm predicts their communication in future. The results of tests on SID dataset as coauthor dataset show that developed algorithm outperforms all the structure-based link prediction algorithms. Finally, the reasons of algorithm’s efficiency are analyzed and presented پرونده مقاله -
دسترسی آزاد مقاله
2 - Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection
Mohammad Pouya Salvati Jamshid Bagherzadeh Mohasefi Sadegh SulaimanyLink prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A maj چکیده کاملLink prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms. پرونده مقاله -
دسترسی آزاد مقاله
3 - روشی نوین برای پیشبینی ارتباط در شبکههای اجتماعی ناهمگن
سعیده رضاوندی شعاعی هادی زارعبا گسترش روزافزون شبکههای اجتماعی، علوم شبکه مورد توجه بسیاری از پژوهشگران در زمینههای مختلف قرار گرفته است. علاوه بر آن بسیاری از مسایل کاربردی مهندسی با استفاده از ابزار شبکههای اجتماعی مدلسازی شدهاند. پیشبینی تغییر و تحول در ساختار شبکههای اجتماعی یکی از مسایل چکیده کاملبا گسترش روزافزون شبکههای اجتماعی، علوم شبکه مورد توجه بسیاری از پژوهشگران در زمینههای مختلف قرار گرفته است. علاوه بر آن بسیاری از مسایل کاربردی مهندسی با استفاده از ابزار شبکههای اجتماعی مدلسازی شدهاند. پیشبینی تغییر و تحول در ساختار شبکههای اجتماعی یکی از مسایل اساسی در تحلیل شبکههای اجتماعی است که با عنوان مسأله پیشبینی ارتباط در علوم شبکه شناخته میشود. امروزه با گسترش استفاده از شبکههای اجتماعی، فعالیت افراد در قالب چندین شبکه با عنوان شبکههای اجتماعی ناهمگن رواج پیدا کرده است. پیشبینی ارتباط در شبکههای اجتماعی ناهمگن را میتوان بر اساس اطلاعات اضافی موجود نسبت به روشهای قبلی مورد بهبود قرار داد. در رویکرد پیشنهادی این مقاله، ابتدا یک معیار شباهت جدید برای کاربران در شبکههای ناهمگن بر اساس توسعه روشهای مطرح پیشین و با در نظر گرفتن ارتباط بین لایههای مختلف معرفی میشود، سپس با استفاده از رویکرد یادگیری باناظر و بهرهگیری از ویژگیهای تولیدشده بر مبنای معیار شباهت معرفیشده، الگوریتم پیشنهادی مورد تشریح قرار میگیرد. برای ارزیابی روش پیشنهادی از معیارهای استاندارد همانند دقت و صحت بهره گرفتهایم. مقایسه روش پیشنهادی با روشهای شناختهشده پیشین بر روی مجموعه دادههای مختلف نشان میدهد که روش پیشنهادی ما برای پیشبینی ارتباط از عملکرد بهتر و مطلوبتری برخوردار است به طوری که از نظر صحت تا ۲۰ درصد موجب بهبود عملکرد شده است. پرونده مقاله -
دسترسی آزاد مقاله
4 - Inferring Diffusion Network from Information Cascades using Transitive Influence
Mehdi Emadi Maseud Rahgozar Farhad OroumchianNowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a so چکیده کاملNowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a social network, a diffusion process occurs. Each node of a network that participates in the diffusion process leaves some effects on this process, such as its transmission time. In most cases, despite the visibility of such effects of diffusion process, the structure of the network is unknown. Knowing the structure of a social network is essential for many research studies such as: such as community detection, expert finding, influence maximization, information diffusion, sentiment propagation, immunization against rumors, etc. Hence, inferring diffusion network and studying the behavior of the inferred network are considered to be important issues in social network researches. In recent years, various methods have been proposed for inferring a diffusion network. A wide range of proposed models, named parametric models, assume that the pattern of the propagation process follows a particular distribution. What's happening in the real world is very complicated and cannot easily be modeled with parametric models. Also, the models provided for large volumes of data do not have the required performance due to their high execution time. However, in this article, a nonparametric model is proposed that infers the underlying diffusion network. In the proposed model, all potential edges between the network nodes are identified using a similarity-based link prediction method. Then, a fast algorithm for graph pruning is used to reduce the number of edges. The proposed algorithm uses the transitive influence principle in social networks. The time complexity order of the proposed method is O(n3). This method was evaluated for both synthesized and real datasets. Comparison of the proposed method with state-of-the-art on different network types and various models of information cascades show that the model performs better precision and decreases the execution time too. پرونده مقاله