Due to many technological advancements, the amount of connected data drastically increased in the last decade. The analysis of this data and the insights it generates show great potential for supporting decision making processes in various industries and aspects of our lives. Multiple visual analytics solutions have been proposed to gain further insights into such data and gain explainable results. However, the majority of existing solutions are either closed sourced, not available or no longer developed. To mitigate the issues above and based on findings from expert interviews conducted using an existing tool, this paper introduces Collaboration Spotting X, a new network-based interactive visual analytics and information retrieval tool prototype. This prototype enables users to explore connected network datasets such as social network data and bibliometric data using multiple visual cues and interactions. Furthermore, to gain an insight into how this prototype is perceived by users and identify further improvements, a preliminary study with a class of 37 computer science graduate students is described. The study findings show that the students perceive Collaboration Spotting X as a useful tool that helps them complete tasks through visualisation and interaction. Additionally, multiple aspects were identified that might have caused users to experience in addition to positive emotions also some negative emotions during usage. These aspects might have also contributed to a lower usability score. Finally, multiple improvement directions have been identified, which will be implemented in future developments.