The paper we look at here is called “Mining Social Networks Using Heat Diffusion Processes for Marketing Candidates Selection” and is by Yang, Liu and King from The Chinese University of Hong kong.
Companies have started social networks more and more for WOM promotion, increase bran awareness, attract potential clients and so on. This is openly apparent in Facebook pages, Twitter, collaborative filtering, blogs and many many others. This paper presents a model enabling marketers to make the best use of these networks using the “Heat Diffusion Process” (which is an idea borrow from the field of physics). They have 3 models and 3 algorithms to demonstrate that allow for marketing samples to be collected.
In physics the heat diffusion model states that heat flows from a position of high temperature to one of low temperature.
They show that these methods allow us to select the best marketing candidates using the clustering properties of social networks, the planning of a marketing strategy sequentially in time, and they construct a model that can diffuse negative and positive comments about products and brands to simulate the complex discussions within social networks. They want to use their work to help marketeers and companies to defend themselves against negative comments. The idea is to get a subset of individuals to adopt a new product or service based on a potential network of customers.
The heat diffusion model has a time dependant property which means that it can simulate product adoptions step by step. The selection algorithms can represent the clustering coefficient of real social networks. All users of social networks can diffuse comments that can influence other users. Based on this they say that nodes 1 and 2 represent adopters and the heat reaches nodes 3,4 and 5 as time elapses. The users in the trust circle of other users have a greater influence. Not all of the people in the trust circle however will be contacted about the heat source. Also some users are more active than others in diffusing information. they observe that bad news or negative comments diffuse much faster than other news.
Individuals are selected as seed for heat diffusion. The influence of individuals is based on the number of individuals they influence. The heat vector they construct decides on the amount of heat needed for each source. In order for them to diffuse properly they need a lot of heat. Thermal conductivity is then calculated. It sets the heat diffusion rate. The adoption threshold is then set, as if one consumers heat value is higher than others, they are likely to adopt this product.
If a user doesn’t like the product, s/he is allocated negative heat as they will diffuse negative comments. At some point someone in the network will provide different information which might be positive. If that user adopts the product anyway, they will diffuse positive comments. Two defense candidates are then selected and then the negative impact is alleviated.
“So far, our work considers social network as a static network only, and ignores newcomers, new relationships between existing members and the growth of the network’s size. In the future, we plan to consider the evolution property of social networks, and permit our social network to grow at a certain rate”
Why should you care?
This paper shows a new and very different way of analyzing social networks. It gives you a nice opening ti discussions concerning this in a different light. The current methods used in business are not foolproof and such research shows us how simplistic they are and how they can actually be misleading.