{"id":25847,"date":"2024-02-20T10:20:50","date_gmt":"2024-02-20T11:20:50","guid":{"rendered":"https:\/\/express24.ir\/d\/?post_type=product&p=25847"},"modified":"2024-02-25T18:24:09","modified_gmt":"2024-02-25T19:24:09","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%aa%d8%b4%d8%ae%db%8c%d8%b5-%d8%ac%d8%a7%d9%85%d8%b9%d9%87-%d8%af%d8%b1-%d9%85%d8%af%d9%84-%d8%a8%d9%84%d9%88%da%a9-%d8%aa%d8%b5%d8%a7%d8%af%d9%81%db%8c-%da%86%d9%86","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%aa%d8%b4%d8%ae%db%8c%d8%b5-%d8%ac%d8%a7%d9%85%d8%b9%d9%87-%d8%af%d8%b1-%d9%85%d8%af%d9%84-%d8%a8%d9%84%d9%88%da%a9-%d8%aa%d8%b5%d8%a7%d8%af%d9%81%db%8c-%da%86%d9%86\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 \u062a\u0634\u062e\u06cc\u0635 \u062c\u0627\u0645\u0639\u0647 \u062f\u0631 \u0645\u062f\u0644 \u0628\u0644\u0648\u06a9 \u062a\u0635\u0627\u062f\u0641\u06cc \u0686\u0646\u062f \u0645\u0646\u0638\u0648\u0631\u0647"},"content":{"rendered":"
\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nCommunity Detection in the Multi-View Stochastic Block Model<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0634\u062e\u06cc\u0635 \u062c\u0627\u0645\u0639\u0647 \u062f\u0631 \u0645\u062f\u0644 \u0628\u0644\u0648\u06a9 \u062a\u0635\u0627\u062f\u0641\u06cc \u0686\u0646\u062f \u0645\u0646\u0638\u0648\u0631\u0647 <\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nYexin Zhang, Zhongtian Ma, Qiaosheng Zhang, Zhen Wang, Xuelong Li<\/td>\n<\/tr>\n
\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\nPDF<\/td>\n<\/tr>\n
\u0686\u06a9\u06cc\u062f\u0647 <\/td>\nThis paper considers the problem of community detection on multiple potentially correlated graphs from an information-theoretical perspective. We first put forth a random graph model, called the multi-view stochastic block model (MVSBM), designed to generate correlated graphs on the same set of nodes (with cardinality $n$). The $n$ nodes are partitioned into two disjoint communities of equal size. The presence or absence of edges in the graphs for each pair of nodes depends on whether the two nodes belong to the same community or not. The objective for the learner is to recover the hidden communities with observed graphs. Our technical contributions are two-fold: (i) We establish an information-theoretic upper bound (Theorem~1) showing that exact recovery of community is achievable when the model parameters of MVSBM exceed a certain threshold. (ii) Conversely, we derive an information-theoretic lower bound (Theorem~2) showing that when the model parameters of MVSBM fall below the aforementioned threshold, then for any estimator, the expected number of misclassified nodes will always be greater than one. Our results for the MVSBM recover several prior results for community detection in the standard SBM as well as in multiple independent SBMs as special cases.<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n12<\/td>\n<\/tr>\n
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\u0641\u0648\u0642\u0627\u0646\u06cc-\u0646\u0638\u0631\u06cc \u0627\u0637\u0644\u0627\u0639\u0627\u062a (\u0642\u0636\u06cc\u0647 1 ~) \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u0628\u0647\u0628\u0648\u062f\u06cc \u062f\u0642\u06cc\u0642 \u062c\u0627\u0645\u0639\u0647 \u0647\u0646\u06af\u0627\u0645\u06cc \u06a9\u0647 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u062f\u0644 MVSBM \u0627\u0632 \u06cc\u06a9 \u0622\u0633\u062a\u0627\u0646\u0647 \u062e\u0627\u0635 \u0641\u0631\u0627\u062a\u0631 \u0645\u06cc \u0631\u0648\u0646\u062f \u060c \u0642\u0627\u0628\u0644 \u062f\u0633\u062a\u06cc\u0627\u0628\u06cc \u0627\u0633\u062a.(\u0628) \u0628\u0631\u0639\u06a9\u0633 \u060c \u0645\u0627 \u06cc\u06a9 \u0645\u062d\u062f\u0648\u062f\u0647 \u067e\u0627\u06cc\u06cc\u0646 \u062a\u0631-\u0646\u0638\u0631\u06cc \u0627\u0637\u0644\u0627\u0639\u0627\u062a (\u0642\u0636\u06cc\u0647 ~ 2) \u0628\u0647 \u062f\u0633\u062a \u0645\u06cc \u0622\u0648\u0631\u06cc\u0645 \u06a9\u0647 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u0648\u0642\u062a\u06cc \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u062f\u0644 MVSBM \u0632\u06cc\u0631 \u0622\u0633\u062a\u0627\u0646\u0647 \u0641\u0648\u0642 \u0627\u0644\u0630\u06a9\u0631 \u0642\u0631\u0627\u0631 \u0645\u06cc \u06af\u06cc\u0631\u0646\u062f \u060c \u0628\u0631\u0627\u06cc \u0647\u0631 \u0628\u0631\u0622\u0648\u0631\u062f\u06af\u0631 \u060c \u062a\u0639\u062f\u0627\u062f \u0645\u0648\u0631\u062f \u0627\u0646\u062a\u0638\u0627\u0631 \u06af\u0631\u0647 \u0647\u0627\u06cc \u0646\u0627\u062f\u0631\u0633\u062a \u0647\u0645\u06cc\u0634\u0647 \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u06cc\u06a9 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.\u0646\u062a\u0627\u06cc\u062c \u0645\u0627 \u0628\u0631\u0627\u06cc MVSBM \u0686\u0646\u062f\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u0642\u0628\u0644\u06cc \u0631\u0627 \u0628\u0631\u0627\u06cc \u062a\u0634\u062e\u06cc\u0635 \u062c\u0627\u0645\u0639\u0647 \u062f\u0631 SBM \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u062f\u0631 SBM \u0647\u0627\u06cc \u0645\u0633\u062a\u0642\u0644 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u0648\u0627\u0631\u062f \u062e\u0627\u0635 \u0628\u0627\u0632\u06cc\u0627\u0628\u06cc \u0645\u06cc \u06a9\u0646\u062f.<\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nSocial and Information Networks,Information Theory,Machine Learning,Signal Processing,\u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0627\u062c\u062a\u0645\u0627\u0639\u06cc \u0648 \u0627\u0637\u0644\u0627\u0639\u0627\u062a\u06cc \u060c \u062a\u0626\u0648\u0631\u06cc \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u060c \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u060c \u067e\u0631\u062f\u0627\u0632\u0634 \u0633\u06cc\u06af\u0646\u0627\u0644 \u060c<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 17 January, 2024; originally announced January 2024. , Comments: Submitted to IEEE for possible publication<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 17 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: \u0628\u0631\u0627\u06cc \u0627\u0646\u062a\u0634\u0627\u0631 \u0627\u062d\u062a\u0645\u0627\u0644\u06cc \u0628\u0647 IEEE \u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
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\r\n\u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0645\u0642\u0627\u0644\u0647 \u0627\u0635\u0644\u06cc \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u06a9\u0647 \u062f\u0631\u06cc\u0627\u0641\u062a \u0645\u06cc \u06a9\u0646\u06cc\u062f\u060c \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\u200c\u062a\u0631 \u0648 \u062a\u0633\u0644\u0637 \u06a9\u0627\u0645\u0644 \u0628\u0631 \u0645\u0628\u0627\u062d\u062b<\/strong> \u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0627\u06cc \u0627\u0632 \u06a9\u062a\u0627\u0628\u200c\u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0646\u06cc\u0632 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n<\/p>\r\n\r\n