Innovations in network visualization software over the last decade or so have been important to the popularization of social network analysis (SNA) among academics, consultants and managers. Indeed, there is a growing literature that seeks to demonstrate how 'invisible social networks' might be revealed and leveraged for 'visible results' through management interventions. However, the seductive power of the network graphic has distracted attention away from a variety of emerging and long recognized concerns in SNA. For example, weaknesses exist in data collection techniques that often rely on nominal boundary-setting and respondent recall. Non-response can also be highly problematic. Increasingly, email data are being employed, yet this represents a poor proxy for relationships and raises issues of privacy. In displaying relational data, visualizations typically reify and ossify the network. Yet, individual perceptions of a network can vary greatly from unified visualizations, and their structure is typically fleeting. The aim of this paper is to draw together the diffuse literature concerning data input and visual output issues in SNA, in order to raise awareness among management researchers and practitioners. In doing so, the nature and impact of such weaknesses are discussed, as are ways in which these might be resolved or mitigated.