Community detection of temporal (time-evolving) bipartite networks is challenging because it can be performed either on the temporal bipartite network, or on various projected networks, composed of only one type of nodes, via diverse community detection algorithms. In this paper, we aim to systematically design detection methods addressing both network choices and community detection algorithms, and to compare the community structures detected by different methods. We illustrate our methodology by using a telecommunications network as an example. We find that three methods proposed identify evident community structures: one is performed on each snapshot of the temporal network, and the other two, in temporal projections. We characterise the community structures detected by each method by an evaluation network in which the nodes are the services of the telecommunications network, and the weight of the links between them are the number of snapshots that both services were assigned to the same community. Analysing the evaluation networks of the three methods reveals the similarity and difference among these methods in identifying common node pairs or groups of nodes that often belong to the same community. We find that the two methods that are based on the same projected network identify consistent community structures, whereas the method based on the original temporal bipartite network complements this vision of the community structure. Moreover, we found a non-trivial number of node pairs that belong consistently to the same community in all the methods applied.

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Networks are omnipresent and universal. Mankind, for example, forms a social network. Today, information and communication technology (ICT) exponentially accelerates the interaction between the human nodes of this global service network. In that way, ICT appears to evoke a phase transformation, similar to the physical phase transitions of a thermodynamic system which characteristise the transformation of matter. Finding that networks are omnipresent and universal, gives a cause for optimism! Indeed, we can profit from the network knowledge in the ICT sector by translatind this knowledge horizontally to, for example, other nodes in the sector network of our economy and society. Transferring the knowledge vertically to networks on other aggregation levels, such as the human network, could reward us with astounding insights into the developments for the next 100 years. In addition, awareness of the possbile scenarios for the 21st century, and keys for the choice between and control of them, come within reach. In this article, we postulate these exciting visions by travelling through time and on different aggregation levels, discovering some maverick phenomena of transient networks that deepen our understanding of the relevant riddles of our existence. The relatively new ICT sector, enabling connections between all communicating entities, obviously plays a crucial multifold role in this game.@en