دانلود و نمایش مقالات مرتبط با Link Prediction::صفحه 1
دانلود بهترین مقالات isi همراه با ترجمه فارسی
نتیجه جستجو - Link Prediction

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 The role of location and social strength for friendship prediction in location-based social networks
نقش مکان و قدرت اجتماعی برای پیش بینی دوستی در شبکه های اجتماعی مبتنی بر مکان-2018
Recent advances in data mining and machine learning techniques are focused on exploiting location data. These advances, combined with the increased availability of location-acquisition technology, have encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existing methods; 2) put forward a proposal of five new methods addressing gaps identified in our survey while striving to find a balance between optimizing computational resources and improving the predictive power; and 3) used a comprehensive evaluation to quantify the prediction abilities of ten current methods and our five proposals and selected the top-5 friendship prediction methods for LBSNs. We thus present a general panorama of friendship prediction task in the LBSN domain with balanced depth so as to facilitate research and real-world application design regarding this important issue.
keywords: Location-based social networks| Link prediction| Friendship recommendation| Human mobility| User behavior
مقاله انگلیسی
2 A balanced modularity maximization link prediction model in social networks
مدل پیش بینی پیوند حداکثر سازگاری مدولار در شبکه های اجتماعی-2017
Link prediction has been becoming an important research topic due to the rapid growth of social networks. Community-based link prediction methods are proposed to incorporate community information in order to achieve accurate prediction. However, the performance of such methods is sensitive to the selection of community detection algorithms, and they also fail to capture the correlation between link formulation and community evolution. In this paper we introduce a balanced Modularity-Maximization Link Prediction (MMLP) model to address this issue. The idea of MMLP is to integrate the formulation of two types of links into a partitioned network generative model. We proposed a probabilistic algo rithm to emphasize the role of innerLinks, which correspondingly maximizes the network modularity. Then, a trade-off technique is designed to maintain the network in a stable state of equilibrium. We also present an effective feature aggregation method by exploring two variations of network features. Our proposed method can overcome the limit of sev eral community-based methods and the extensive experimental results on both synthetic and real-world benchmark data demonstrate its effectiveness and robustness.
Keywords: Link prediction | Social network | Community detection | Modularity
مقاله انگلیسی
3 A utility-based link prediction method in social networks
یک روش پیش بینی پیوند مبتنی بر ابزار در شبکه های اجتماعی-2017
Link prediction is a fundamental task in social networks, with the goal of estimating the likelihood of a link between each node pair. It can be applied in many situations, such as friend discovery on social media platforms or co-author recommendations in collaboration networks. Compared to the numerous traditional methods, this paper introduces utility analysis to the link prediction method by considering that individual preferences are the main reason behind the decision to form links, and meanwhile it also focuses on the meeting process that is a latent variable during the process of forming links. Accordingly, the link prediction problem is formulated as a machine learning process with latent variables; therefore, an Expectation–Maximization (EM, for short) algorithm is adopted and further developed to cope with the estimation problem. The performance of the present method is tested both on synthetic networks and on real-world datasets from social media networks and collaboration networks. All of the computational results illustrate that the proposed method yields more satisfying link prediction results than the selected benchmarks, and in particular, logistic regression, as a special case of the proposed method, provides the lower boundary of the likelihood function.
Keywords: Networks | Link prediction | Utility analysis | EM algorithm | Latent variable
مقاله انگلیسی
4 A scalable method for link prediction in large real world networks
یک روش مقیاس پذیر برای پیش بینی لینک در شبکه های بزرگ دنیای واقعی-2017
Link prediction has become an important task, especially with the rise of large-scale, complex and dynamic networks. The emerging research area of network dynamics and evolution is directly related to predicting new interactions between objects, a possibility in the near future. Recent studies show that the precision of link prediction can be improved to a great extent by including community information in the prediction methods. As traditional community-based link prediction algorithms can run only on stand alone computers, they are not well suited for most of the large networks. Graph parallelization can be one solution to such problems. Bulk Synchronous Parallel (BSP) programming model is a recently emerged framework for parallelizing graph algorithms. In this paper, we propose a hybrid similarity measure for link prediction in real world networks. We also propose a scalable method for community structure-based link prediction on large networks. This method uses a parallel label propagation algorithm for community detection and a parallel community information-based Adamic–Adar measure for link prediction. We have developed these algorithms using Bulk Synchronous Parallel programming model and tested them with large networks of various domains.
Keywords: Parallel computing | Community structure | Link prediction | Bulk synchronous parallel | Social networks
مقاله انگلیسی
5 روش جدید پیش بینی پیوند سری های زمانی: روش اتوماتای یادگیر
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 27
پیش بینی پیوند یک چالش بزرگ در شبکه های اجتماعی است که از ساختار شبکه ای برای پیش بینی پیوندهای آتی استفاده می کند. روش های رایج پیش بینی پیوند برای پیش بینی پیوندهای مخفی از نمایش گراف ایستا استفاده می کنند که در آن تصویری از شبکه برای یافتن پیوندهای آتی یا مخفی مورد استفاده قرار می گیرد. برای مثال، پیش بینی پیوند مبتنی بر معیار تشابه، روش سنتی رایجی است که معیار تشابه را برای تمامی پیوندهای غیرمتصل محاسبه نموده، پیوندها را براساس معیارهای تشابه آنها مرتب نموده و پیوندهای با امتیاز تشابه بالاتر را به عنوان پیوندهای آتی برچسب گذاری می کند. از آنجاکه فعالیت های افراد در شبکه های اجتماعی، پویا و غیرقطعی است، و ساختار شبکه ها با گذشت زمان تغییر می کند، استفاده از گراف های قطعی برای مدلسازی و تحلیل شبکه ی اجتماعی نمی تواند روش مناسبی باشد. در مسأله ی پیش-بینی پیوند سری های زمانی، احتمال وقوع پیوند سری های زمانی برای پیش بینی پیوندهای آتی مورد استفاده قرار می گیرد. ما در این مقاله یک روش پیش بینی پیوند سری های زمانی مبتنی بر اتوماتای یادگیر را پیشنهاد می کنیم. در الگوریتم پیشنهادی برای هر پیوندی که قرار است پیش بینی شود، یک اتوماسیون یادگیری داریم و هر اتوماسیون یادگیری در تلاش است وجود یا عدم وجود پیوند متناظر را پیش بینی کند. برای پیش بینی احتمال وقوع پیوند در زمان T، یک دنباله ی متشکل از مراحل 1 تا T-1 داریم و اتوماسیون یادگیری این مراحل را می پیماید تا وجود یا عدم وجود پیوند مربوطه را بیاموزد. زمانیکه احتمال وقوع پیوند سری های زمانی را در نظر بگیریم، آزمایشات اولیه ی پیش بینی پیوند با شبکه های ایمیل و نویسندگی مشترک، نتایج رضایت بخشی را فراهم می آورد.
کلیدواژه ها: شبکه ی اجتماعی | پیش بینی پیوند | سری های زمانی | اتوماتای یادگیر
مقاله ترجمه شده
6 A deep dive into location-based communities in social discovery networks
شیرجه رفتن عمیق به جوامع مبتنی بر مکان در کشف شبکه های اجتماعی-2017
Location-based social discovery networks (LBSD) is an emerging category of location-based social net works (LBSN) that are specifically designed to enable users to discover and communicate with nearby people. In this paper, we present the first measurement study of the characteristics and evolution of location-based communities which are based on a social discovery network and geographic proximity. We measure and analyse more than 176K location-based communities with over 1.4 million distinct members of a popular social discovery network and more than 46 million locations. We characterise the evolution of the communities and study the user behaviour in LBSD by analysing the mobility features of users belonging to communities in comparison to non-community members. Using observed spatio-temporal similarity features, we build and evaluate a classifier to predict location-based community membership solely based on user mobility information.
Keywords: Human mobility | Link prediction | Social discovery networks
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی