Maximizing product adoption in social networks

One of the key objectives of viral marketing is to identify a small set of users in a social network, who when convinced to adopt a product will influence others in the network leading to a large number of adoptions in an expected sense. The seminal work of Kempe et al. [13] approaches this as the problem of influence maximization. This and other previous papers tacitly assume that a user who is influenced (or, informed) about a product necessarily adopts the product and encourages her friends to adopt it. However, an influenced user may not adopt the product herself, and yet form an opinion based on the experiences of her friends, and share this opinion with others. Furthermore, a user who adopts the product may not like it and hence not encourage her friends to adopt it to the same extent as another user who adopted and liked the product. This is independent of the extent to which those friends are influenced by her. Previous works do not account for these phenomena. We argue t...

• Type: Journal
• Year: 2012
• Where: WSDM
• Authors: Smriti Bhagat, Amit Goyal 0002, Laks V. S. Lakshmanan
• Keywords: Approximation Algorithm, Data Mining, Linear Threshold, Lt Model, WSDM 2012