دانلود مقاله شبکه های اجتماعی علامت گذاری شده
چکیده
1. مقدمه
2. آثار مرتبط
3. مطالب و روش ها
4. ویژگی های رفتاری C-MSN
5. نتایج تجربی
6. نتیجه گیری
منابع
Abstract
1. Introduction
2. Related works
3. Material and methods
4. Behavioral properties of C-MSN
5. Experimental results
6. Conclusions
Declaration of Competing Interest
References
چکیده
شبکه‌های اجتماعی، سیستم‌های اشتراک‌گذاری اطلاعات الکترونیکی هستند، به همین دلیل مطالعات زیادی در مورد شبکه‌های اجتماعی صورت گرفته است. هنگام مطالعه شبکه های اجتماعی، می توان از روش های حل مبتنی بر متن استفاده کرد. این نوع مطالعه خارج از محدوده این مقاله است. برخی از مطالعات از مدل‌های ریاضی مانند نمودارها استفاده کرده‌اند و نمودارها مدل‌های ریاضی برای نمایش بسیاری از چیزها هستند و شبکه‌های اجتماعی یکی از آنهاست. با این حال، نمودارها مدل های ثابتی هستند که ساختار آنها نمی تواند با رفتارهای شبکه های اجتماعی مطابقت داشته باشد. برای رهایی از این مورد، در برخی از مطالعات اخیر از شبکه های پتری استفاده شده است، اما دارای کاستی هایی هستند (مدل های به دست آمده کامل نیستند). به همین دلیل، شبکه های اجتماعی را با استفاده از شبکه های پتری مدل سازی کردیم. مدل به دست آمده شبکه اجتماعی مشخص شده نام دارد. شبکه اجتماعی علامت گذاری شده دارای دو نوع است مانند شبکه اجتماعی علامت گذاری شده همزمان و شبکه اجتماعی با علامت گذاری موازی. مدل‌های به‌دست‌آمده از نظر ویژگی‌های رفتاری و ساختاری مورد تجزیه و تحلیل قرار گرفتند و ویژگی‌های اصلی مدل مشخص شد. تمام این خواص در این مطالعه توضیح داده شده است.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Social networks are electronically information sharing systems, due to this case, there are many studies on social networks. When studying social networks, text-based solution methods can be used; this type of study is outside the scope of this paper. Some studies have used mathematical models such as graphs, and graphs are mathematical models to represent many things, and social networks are one of them. However, graphs are static models whose structure cannot match the behaviors of social networks. To get rid of this case, Petri nets have been used in some recent studies , however, they have some deficiencies (obtained models are not complete and sound). Because of this case, we modeled social networks by using Petri nets. The resulting model is called Marked Social Network. The marked social network has two types such as Concurrent Marked Social Network and Parallel Marked Social Network. The obtained models were analyzed in case of behavioral and structural properties, and the major properties of the model were determined. All these properties are described in this study.
Introduction
Social networks were developed after electronics information sharing systems coming out, and social networks are modeled by using graphs. Due to the interests of users and capabilities of social networks, this area is an important emerging area, so, there are many studies of social networks such as community detection, stance detection, privacy-preserving proximity detection, anomaly detection, irony/sarcasm detection, role mining, topic/event detection, and causality detection.
1.1. Community detection
Community detection is the problem to detecting groups in networks whose characteristics are similar and they are tightly-coupled [3]. In other words, a community can be also described as “a group of entities/that are in proximity of each other when compared to other entities of remaining networks” [3]. The community detection can be handled by using clique detection in the graph which is a mathematical model of the related social network, or compact group discovery can be handled by using graphs [8], [11].
1.2. Stance detection
Stance detection is a social network issue that illustrates that an individual who gave an opinion about a certain target is neutral, against, or favor towards the target. In another word, stance detection can be regarded as opinion mining or sentiment analysis [13].
Conclusions
The social networks are modeled by using graphs; however, graphs are static models and they cannot model the dynamic properties of social networks. Due to this case, we modeled social networks by using Petri nets. The obtained Petri net model was named Marked Social Networks. The Marked Social Networks have two types such as Concurrent Marked Social Network and Parallel Marked Social Network. The major properties of these networks were analyzed in this paper. An important point is that both models (C-MSN and P-MSN) are deadlock-free, so they can be used to model real-life applications. However, the P-MSN model is more suitable because of the waiting times in the C-MSN model.
In this study, a general mathematical model for social networks is presented. This model can be customized for different social networks. In future studies, this model will be expanded and applied for different analyzes on various social network groups.