As brands’ use of social media to connect with customers becomes increasingly important, there is a need to continually monitor and examine activities in social networks. An important aspect of the social network communications is its unique ways of concentrating and dispersing information among participants (actors) – density and centrality of the network. By looking at both density and centrality, the relationships among actors and their ability to influence others are revealed, allowing deeper understanding into networked behaviors. In this manner, examining whole and ego network patterns, the unique roles of individual actors, can provide brands significant insight in understanding how influencers form and how users connect and spread information. Based on the social network analysis, which represents a combination of theory and analytical methods of networked relationships, this study analyzed Twitter networks of two multi-brand cosmetics and beauty retailers, Sephora (global brand) and Ulta Beauty (U.S. domestic brand). Using NodeXL, daily Twitter data for both brands were gathered to investigate network activities. By examining both ego-networks and the whole networks, the results showed that while ego-networks for brands were quite similar to one another, there was a big difference between the ego-networks and whole networks in regards to the number of actors, type of connectivity, as well as the prominence of brands. Sephora was often not an important part of its hashtag network, and thus was not able to maintain strong control over communications and messages in these networks whereas Ulta maintained its control over its networks. The findings from analyzing these network patterns, the unique roles of individual actors, and the brands within the networks provide significant insights in understanding how influencers form and develop the ability to connect and spread information.