A Framework To Identify Influencers In Signed Social Networks

Social networks are defined as a graphical data structure, which captures complex social interactions between users of a social network. Signed social networks are weighted representations of the social network with the emphasis of capturing both positive and negative interactions (edges) between actors of the network. Ad-hoc communities in a social network, as a corollary can be treated as the logical grouping of social actors that share common interests, ideas, or beliefs. In this work, we leverage these known constructs in social networks to effectively identify influencers (i.e. a subset of actors that exert their influence over a community), aka, seeds. Traditional approaches largely rely on degree of connectivity in identifying influencers of a community. We hypothesize that there are other measures to identify influences. In this work, our objective is therefore to explore and propose a technique using Principal Component Analysis (PCA) to identify the smallest set of influencers with increasing the possibility of adopting a product. Furthermore, we validate our finding by evaluating the potential of these influencers to identify positive communities in a social network. We believe our approach is novel in choosing our influencers (seeds) and thus by using these seeds, positive and negative edges are established. We exploit resulting positive and negative edges to mine ad-hoc communities of interest.