  A community is sub-network inside P2P networks that partition the network
into groups of similar peers to improve performance by reducing network traffic
and high search query success rate. Large communities are common in online
social networks than traditional file-sharing P2P networks because many people
capture huge amounts of data through their lives. This increases the number of
hosts bearing similar data in the network and hence increases the size of
communities. This article presents a Memory Thread-based Communities for our
entity-based social P2P network that partition the network into groups of peers
sharing data belonging to an entity - person, place, object or interest, having
its own digital memory or be a part another memory. These connected peers
having further similarities by organizing the network using linear orderings. A
Memory-Thread is the collection of digital memories having a common reference
key and organized according to some form of correlation. The simulation results
show an increase in network performance for the proposed scheme along with a
decrease in network overhead and higher query success rate compared to other
similar schemes. The network maintains its performance even while the network
traffic and size increase.
