Clustered embedding of massive social networks
نویسندگان
چکیده
منابع مشابه
Embedding Identity and Interest for Social Networks
Network embedding fills the gap of applying tuple-based data mining models to networked datasets through learning latent representations or embeddings. However, it may not be likely to associate latent embeddings with physical meanings just as the name, latent embedding, literally suggests. Hence, models built on embeddings may not be interpretable. In this paper, we thus propose to learn ident...
متن کاملEconomic Influence in Massive Online Social Networks
1 Introduction We use an unprecedented data set to examine the extent to which networked social relationships between consumers explain, and in fact influence, patterns of user behavior, e-commerce service adoption, online demand and ultimately revenue generation. Our data include a global instant messaging (IM) network of 27 million users from one of the largest online portals in the world, co...
متن کاملPeer Ratings in Massive Online Social Networks
Instant quality feedback in the form of online peer ratings is a prominent feature of modern massive online social networks (MOSNs). It allows network members to indicate their appreciation of a post, comment, photograph, etc. Some MOSNs support both positive and negative (signed) ratings. In this study, we rated 11 thousand MOSN member profiles and collected user responses to the ratings. MOSN...
متن کاملDetecting Emotional Contagion in Massive Social Networks
Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affect...
متن کاملLearning and Inference in Massive Social Networks
Researchers and practitioners increasingly are gaining access to data on explicit social networks. For example, telecommunications and technology firms record data on consumer networks (via phone calls, emails, voice-over-IP, instant messaging), and social-network portal sites such as MySpace, Friendster and Facebook record consumer-generated data on social networks. Inference for fraud detecti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ACM SIGMETRICS Performance Evaluation Review
سال: 2012
ISSN: 0163-5999
DOI: 10.1145/2318857.2254796