实现
发表于 2025-3-25 06:09:10
J.-P. Dussault,M. Haddou,T. Migotit words in tweets) are greatly affected by the sparsity of the short tweet texts and the low co-occurrence rates of hashtags in tweets. Meanwhile, semantically related hashtags but using different text-expressions may show similar temporal patterns (i.e., the frequencies of hashtag usages changing
忙碌
发表于 2025-3-25 08:13:45
http://reply.papertrans.cn/71/7034/703325/703325_22.png
屈尊
发表于 2025-3-25 12:29:19
Nidhi Sharma,Jaya Bisht,S. K. Mishran (NMF) based methods have been proved to be effective in the task of community detection. However, real-world networks could be noisy and existing NMF based community detection methods are sensitive to the outliers and noise due to the utilization of the squared loss function to measure the quality
adumbrate
发表于 2025-3-25 16:25:01
http://reply.papertrans.cn/71/7034/703325/703325_24.png
thyroid-hormone
发表于 2025-3-25 21:57:37
http://reply.papertrans.cn/71/7034/703325/703325_25.png
Decimate
发表于 2025-3-26 03:03:09
http://reply.papertrans.cn/71/7034/703325/703325_26.png
自传
发表于 2025-3-26 08:05:36
http://reply.papertrans.cn/71/7034/703325/703325_27.png
热情赞扬
发表于 2025-3-26 10:13:12
Walter Cedric Simo Tao Lee) within the allocated budget whose initial activation leads to the maximum number of influenced nodes. In reality, the influence probability between two users depends upon the context (i.e., tags). However, existing studies on this problem do not consider the tag specific influence probability. To
拔出
发表于 2025-3-26 16:38:46
Vivek Laha,Rahul Kumar,Harsh Narayan Singh,S. K. Mishra) within the allocated budget whose initial activation leads to the maximum number of influenced nodes. In reality, the influence probability between two users depends upon the context (i.e., tags). However, existing studies on this problem do not consider the tag specific influence probability. To
ACTIN
发表于 2025-3-26 17:16:43
Balendu Bhooshan Upadhyay,Priyanka Mishratagged events. These event-traces often manifest in hidden (possibly overlapping) communities of users with similar interests. Inferring these implicit communities is crucial for forming user profiles for improvements in recommendation and prediction tasks. Given only time-stamped geo-tagged traces