کشف جوامع در شبکه‌های اجتماعی با استفاده از کاوش الگوی تکرارشونده
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسیسید احمد موسوی 1 , مهرداد جلالی 2 , نگین میثاقیان 3
1 - گروه کامپیوتر
2 - دانشگاه آزاد اسلامی، مشهد
3 - گروه کامپیوتر
کلید واژه: Social networks, شبکه‌های اجتماعی, Community Detection, کشف جامعه, کاوش الگوی تکرارشونده, Frequent pattern mining,
چکیده مقاله :
امروزه وب سایت‌های شبکه های اجتماعی به یک منبع غنی از داده های ناهمگون مبدل شده است؛ از این رو تجزیه و تحلیل این داده‌ها می‌تواند منجر به کشف اطلاعات و روابط ناشناخته در این شبکه‌ها شود. کشف جامعه متشکل از گره های مشابه یک چالش مهم در زمینه تجزیه و تحلیل داده های شبکه های اجتماعی است، و به طور گسترده ای در زمینه ساختار گرافی در این شبکه‌ها مورد مطالعه قرار گرفته است. شبکه های اجتماعی اینترنتی علاوه بر ساختار گرافی، حاوی اطلاعات مفیدی از کاربران درون شبکه می‌باشند؛ که استفاده از این اطلاعات می‌تواند منجر به بهبود کیفت کشف جوامع گردد. در این مقاله یک روش به منظور کشف جامعه ارائه شده است که علاوه بر اطلاعات ارتباطی بین گره ها از اطلاعات محتوایی به منظور ارتقا کیفیت کشف جوامع استفاده می‌گردد. این روش یک رویکرد جدید مبتنی بر الگوی تکرار شونده و بر اساس عملیات کاربران در شبکه است و به طور خاص، بر روی شبکه های اجتماعی اینترنتی که در آن کاربران عملیات مورد علاقه خود را انتخاب می کنند، اجرا می‌شود. ابتدا، بر اساس علایق و یا فعالیت های کاربران در شبکه، تعدادی جوامع کوچک متشکل از کاربران مشابه را کشف می کنیم و سپس با استفاده از ارتباطات اجتماعی هر جامعه را گسترش می دهیم. نتایج ارزیابی F-measure بر روی دو مجموعه داده دنیای واقعی (بلاگ کاتالوگ و فلیکر) نشان می‌دهد که روش پیشنهادی منجر به بهبود کیفیت کشف جوامع می شود.
recently, the website social networks provide a rich resource of Heterogeneous data which the analysis of these data can lead to discover unknown information and relations in these networks. The discovery of community including “similar” nodes is a challenging issue in analysis of social networks data, and it has widely studied in the social networking community in the context of the structure of the underlying graphs. The online social networks, additionally having graph structures, include effective information of users within networks, which using this information can lead to improve the quality of communities' discovery. In this paper, for detecting community, a method is provided that in addition to the communication among nodes, to improve the quality of the discovered communities, content information is used, as well. This method is a new approach based on a frequent pattern and the actions of users in the network and in particular, is performed on social networking sites where users select their favorite acts. First, based on Interests and activities of users in networks, we discover some small communities of similar users, and then by using social relations, extend (those) communities. The F-measure evaluated results on two real-world datasets (Blogcatalog and Flicker) demonstrate that the proposed method leads to improvethe quality of community detection.
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