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Depending on the density of the network, it could happen that multiple nodes belong to the same slot.
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However, their work can detect P2P bots inside a network only when there are multiple infected nodes belonging to the same botnet.
These algorithms identify the bridge nodes at the beginning, i.e. nodes belong to multiple clusters, and then either duplicate or remove the bridge nodes from the network.
However, clustering of edges may be preferred: nodes belong to multiple functional groups, but clustering of nodes typically cannot capture the group overlap, while clustering of edges can.
Thus, nodes that have links to a lot of communities will get pushed into the centre of the circle making this method well suited for representing ego networks where one or a small number of nodes belong to multiple communities (Fig. 1B).
All the other nodes belong to the third category.
Approximately 71% of the nodes belong to a single major component (subnetwork).
OC is the average number of non-trivial clusters that nodes belong to.
The complex network can be divided into multiple sub-networks, such that the nodes belonging to the same sub-network are identical, while the ones belonging to different sub-networks are non-identical.
To avoid the conflicts during data transmission, the gateway nodes need to set up a table of time division multiple access (TDMA) to distribute the time-slot for sensor nodes belonging to its cluster.
This context-sensitive behavior improves the quantitative estimation of the potential correspondence between nodes belonging to different networks, ultimately, improving the overall accuracy of the multiple network alignment as we will demonstrate through extensive performance evaluation based on real and synthetic biological networks.
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