شناسائی انجمن در شبکه های دوبخشی با استفاده از معیار مرکزیت هلرنک
محورهای موضوعی : فناوری اطلاعات و ارتباطات
علی خسروزاده
1
(گروه مهندسی کامپیوتر، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران)
علی موقر
2
(گروه مهندسی کامپیوتر، دانشگاه صنعتی شریف، تهران، ایران)
محمدمهدی گیلانیان صادقی
3
(گروه مهندسی کامپیوتر، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران)
حمیدرضا ماهیار
4
(گروه مهندسی کامپیوتر، دانشگاه مک مستر، همیلتون، انتاریو، کانادا)
کلید واژه: شبکه های اجتماعی, گراف های دوبخشی, معیار مرکزیت, شناسائی انجمن, رای گیری,
چکیده مقاله :
ساختار انجمن ویژگی مشترک و مهمی در بسیاری از شبکه های پیچیده از جمله شبکه های دوبخشی است. شناسائی انجمن ها در سال‌های اخیر در بسیاری زمینه‌ها مورد توجه قرار گرفته و روش‌های زیادی برای این منظور پیشنهاد شده است، اما مصرف سنگین زمان در برخی روش ها، استفاده از آنها را در شبکه‌های بزرگ مقیاس محدود می‌کند. روش‌هائی با پیچیدگی کمتر وجود دارند اما اکثراً غیرقطعی هستند که کاربرد آنها در دنیای واقعی را کاهش می‌دهد. رویکرد معمول اتخاذ شده برای شناسائی انجمن ها در شبکه‌های دوبخشی این است که ابتدا یک طرح ریزی یک‌بخشی از شبکه ساخته شود و سپس انجمن ها در آن طرح ریزی با استفاده از روش‌های مربوط به شبکه‌های یک‌بخشی شناسائی شوند. این طرح ریزی ها به طور ذاتی اطلاعات را از دست می‌دهند. در این مقاله بر اساس معیار ماژولاریتی دوبخشی که قدرت تقسیم بندی ها را در شبکه های دوبخشی محاسبه می کند و با استفاده از معیار مرکزیت هلرنک، روشی سریع و قطعی برای شناسائی انجمن ها از شبکه های دوبخشی بطور مستقیم و بی نیاز از طرح ریزی ارائه گردیده است. روش پیشنهادی از فرآیند رأی گیری در فعالیت های انتخاباتی در جامعه اجتماعی الهام گرفته و آن را شبیه سازی می کند. نتایج آزمایشات نشان می دهد، مقدار ماژولاریتی انجمن های حاصل و دقت شناسائی تعداد آنها در روش پیشنهادی بهبود یافته است.
Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it.
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