Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Một biện pháp đo lường độ tương đồng mới cho dự đoán liên kết trong mạng đơn và mạng lưỡng phân
Tóm tắt
Mạng lưới hiện diện ở khắp mọi nơi và các ứng dụng của chúng trong nhiều lĩnh vực như khoa học máy tính, khoa học sinh học, kinh tế học, và kỹ thuật hóa học đã thu hút sự chú ý của nhiều nhà nghiên cứu. Nhiều hệ thống phức tạp trong thế giới thực có thể được biểu diễn bằng mạng hoặc đồ thị. Dự đoán liên kết là một trong những nhiệm vụ quan trọng nhất trong phân tích mạng, do đó thu hút rất nhiều sự quan tâm nghiên cứu trong những thập kỷ qua. Trong bài báo này, chúng tôi giới thiệu một thuật toán mới cho dự đoán liên kết hoạt động hiệu quả cho cả đồ thị đơn và đồ thị lưỡng phân. Thuật toán mới của chúng tôi dựa trên khái niệm về vector riêng và khoảng cách ngắn nhất giữa các nút. Chúng tôi đã sử dụng định lý Peron–Frobenius về tầm quan trọng của nút cho dự đoán liên kết. Bốn chỉ số, bao gồm AUC, độ chính xác, sức mạnh dự đoán và độ chính xác@K, đã được tính toán và so sánh với mười bốn thuật toán cơ sở để kiểm tra hiệu suất của thuật toán đề xuất. Việc kiểm tra được thực hiện trên mười ba tập dữ liệu, và kết quả thực nghiệm cho thấy phương pháp đề xuất vượt trội hơn so với thuật toán cơ sở dựa trên bốn chỉ số đã cho.
Từ khóa
#mạng lưới #dự đoán liên kết #đồ thị đơn #đồ thị lưỡng phân #thuật toán Peron-FrobeniusTài liệu tham khảo
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