Information-centric networks enables a multitude of nodes, in particular near the end-users, to provide storage and communication. At the edge, nodes can connect with each other directly to get content locally whenever possible. As the topology of the network directly influences the nodes' connectivity, there has been some work to compute the graph centrality of each node within the topology of the edge network. The centrality is then used to distinguish nodes at the edge of the network. We argue that, for a network with caches, graph centrality is not an appropriate metric. Indeed, a node with low connectivity (and thereby low centrality) that caches a lot of content may provide a very valuable role in the network. To capture this, we introduce a popularity-weighted content-based centrality (P-CBC) metric which takes into account how well a node is connected to the content the network is delivering, rather than to the other nodes in the network. To illustrate the validity of considering content-based centrality, we use this new metric for a collaborative caching algorithm. We compare the performance of the proposed collaborative caching with typical centrality based, non-centrality based, and non-collaborative caching mechanisms. Our simulation implements P-CBC on three random instances of large scale realistic network topology comprising 2, 896 nodes with three content replication levels. Results shows that P-CBC outperforms benchmark caching schemes and yields a roughly 3x improvement for the average cache hit rate.